diff --git a/Assignment 1/Assignment1_SakshamAgarwal/SakshamAgarwal_Assignment1_Matplotlib.ipynb b/Assignment 1/Assignment1_SakshamAgarwal/SakshamAgarwal_Assignment1_Matplotlib.ipynb
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+{"cells":[{"cell_type":"markdown","metadata":{"id":"_sfWZU_eST2O"},"source":["**NOTE: ALL THE COMMANDS FOR PLOTTING A FIGURE SHOULD ALL GO IN THE SAME CELL. SEPARATING THEM OUT INTO MULTIPLE CELLS MAY CAUSE NOTHING TO SHOW UP.**\n","\n","# Exercises\n","\n","Follow the instructions to recreate the plots using this data:\n","\n","## Data"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"l0eZ10dLST2X"},"outputs":[],"source":["import numpy as np\n","x = np.arange(0,100)\n","y = x*2\n","z = x**2"]},{"cell_type":"markdown","metadata":{"id":"veY4-lsGST2c"},"source":["**Import matplotlib.pyplot as plt**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"tAOmEcNKST2e"},"outputs":[],"source":["import matplotlib.pyplot as plt"]},{"cell_type":"markdown","metadata":{"id":"5j_39gsVST2f"},"source":["## Exercise 1\n","\n","**Follow along with these steps**\n","* Create a figure object called fig using plt.figure()\n","* Use add_axes to add an axis to the figure canvas at [0,0,1,1]. Call this new axis ax.\n","* Plot (x,y) on that axes and set the labels and titles to match the plot below:"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"03Ts4r5SST2g","outputId":"68623e37-f7dc-4a7f-c0b3-43385a6463fe","colab":{"base_uri":"https://localhost:8080/","height":430},"executionInfo":{"status":"ok","timestamp":1764870355442,"user_tz":-330,"elapsed":274,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["plt.plot(x,y)\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"I8mFrIGwST2i"},"source":["## Exercise 2\n","**Create a figure object and put two axes on it, ax1 and ax2. Located at [0,0,1,1] and [0.2,0.5,.2,.2] respectively.**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"D4nUfU26ST2k","outputId":"4252cfdb-ed2d-49e7-e301-637757613e88","colab":{"base_uri":"https://localhost:8080/","height":546},"executionInfo":{"status":"ok","timestamp":1765028165600,"user_tz":-330,"elapsed":319,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["fig=plt.figure()\n","ax1=fig.add_axes([0,0,1,1])\n","ax2=fig.add_axes([0.2,0.5,.2,.2])\n","plt.show()"]},{"cell_type":"code","source":["fig=plt.figure()\n","ax1=fig.add_axes([0,0,1,1])\n","ax1.plot(x,y,color='r')\n","ax2=fig.add_axes([0.2,0.5,.2,.2])\n","ax2.plot(x,y,color='r')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":540},"id":"b2RgimzH4JTD","executionInfo":{"status":"ok","timestamp":1765030316305,"user_tz":-330,"elapsed":122,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"22f6d7fa-0ba8-4571-c83a-793bcc3a41a3"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAArgAAAILCAYAAAADnu/0AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAARXFJREFUeJzt3X10VOW99vErARJAkokBkyE1KHpswapUQGIItXpIDWhpKRSFYhs1QGsntpBSk/QRqWgNYmtdWJHT87TSgoAdluCRCmthgHA8hogJOdY3yuRQwUKCyklmiE0IZD9/zJMhM0kgLzOZmb2/n7Vmre6XTO5Zezf8vOfavzvGMAxDAAAAgEnEhnsAAAAAQDBR4AIAAMBUKHABAABgKhS4AAAAMBUKXAAAAJgKBS4AAABMhQIXAAAApjIw3APojdbWVh0/flwJCQmKiYkJ93AAAAAQYoZhyOPxKC0tTbGxF56jjcoC9/jx40pPTw/3MAAAANDPjh07pssvv/yC50RlgZuQkCDJ+wETExPDPBoAAACEmtvtVnp6uq8OvJCoLHDbYgmJiYkUuAAAABbSnXgqD5kBAADAVChwAQAAYCoUuAAAADAVClwAAACYCgUuAAAATIUCFwAAAKZCgQsAAABTocAFAACAqVDgAgAAwFQocAEAAGAqFLgAAAAwlR4VuCUlJbrpppuUkJCglJQUzZw5U4cOHfI7p6mpSQ6HQ8OHD9ewYcM0e/Zs1dXV+Z1z9OhR3XnnnRo6dKhSUlL0s5/9TGfPnu37pwEAAIDl9ajALSsrk8Ph0P79+7Vr1y61tLTo9ttvV2Njo++cJUuW6NVXX5XT6VRZWZmOHz+uWbNm+Y6fO3dOd955p86cOaM333xTf/zjH7Vu3To98sgjwftUAAAAsKwYwzCM3v7wJ598opSUFJWVlemWW25RQ0ODLrvsMm3cuFHf+c53JEkffvihxo4dq/Lyct18883asWOHvvGNb+j48eNKTU2VJK1du1aFhYX65JNPFBcXd9Hf63a7ZbPZ1NDQoMTExN4OHwAAAFGiJ/VfnzK4DQ0NkqTk5GRJUmVlpVpaWpSdne07Z8yYMRo1apTKy8slSeXl5br++ut9xa0k5eTkyO1267333uv09zQ3N8vtdvu9AAAAgM70usBtbW3V4sWLlZWVpeuuu06SVFtbq7i4OCUlJfmdm5qaqtraWt857YvbtuNtxzpTUlIim83me6Wnp/d22AAAADC5Xhe4DodD7777rjZv3hzM8XSquLhYDQ0NvtexY8dC/jsBAAAQnXpV4Obn52v79u3as2ePLr/8ct9+u92uM2fOqL6+3u/8uro62e123zmBXRXattvOCRQfH6/ExES/FwAAAMKkqUmK4AnHHhW4hmEoPz9fW7du1e7duzV69Gi/4xMmTNCgQYNUWlrq23fo0CEdPXpUmZmZkqTMzEz99a9/1cmTJ33n7Nq1S4mJibr22mv78lkAAAAQai6XNHmydMcd0uefh3s0nRrYk5MdDoc2btyoV155RQkJCb7MrM1m05AhQ2Sz2ZSXl6eCggIlJycrMTFRDz74oDIzM3XzzTdLkm6//XZde+21+t73vqdVq1aptrZWDz/8sBwOh+Lj44P/CQEAABAcTqeUlyd5PNLw4dLf/iZ95SvhHlUHPWoTFhMT0+n+F154Qffee68k70IPP/3pT7Vp0yY1NzcrJydHa9as8YsffPTRR3rggQe0d+9eXXLJJcrNzdXKlSs1cGD36m3ahAEAAPSjpiZp6VLpuee821OmSJs2Se2iqqHWk/qvT31ww4UCFwAAoJ/U1Eh33SVVVXm3i4ulFSukbk5MBktP6r/+HRkAAACix5Yt3kiC2+2NJGzYIE2bFu5RXVSfFnoAAACACTU1Sfn50pw53uJ2yhSpujoqiluJAhcAAADt1dRIWVnn87ZFRdKePf2at+0rIgoAAADwcjqlBQuiLpIQiBlcAAAAq2uLJNx1V1RGEgJR4AIAAFiZCSIJgYgoAAAAWFVgJGH9emn69HCPqs+YwQUAALCapibJ4TgfScjK8kYSTFDcShS4AAAA1uJySZMnS2vWeLeLiqS9e6M6khCIiAIAAIBVOJ3ehRs8HlNFEgIxgwsAAGB27bskeDznuySYsLiVKHABAADMzYRdEi6GiAIAAIBZmWThhp5iBhcAAMBsTLZwQ09R4AIAAJhJYCShuNj0kYRARBQAAADMwqKRhEDM4AIAAES7wEhC28INFixuJQpcAACA6Na2cEP7SILJFm7oKSIKAAAA0Spw4QaLRhICMYMLAAAQbbpauIHiVhIFLgAAQHQJjCRYYOGGniKiAAAAEC0CIwnr15t2ud2+YAYXAAAg0nUVSaC47RQFLgAAQCQjktBjRBQAAAAiFZGEXmEGFwAAINIQSegTClwAAIBIUlPjXYms/cINRBJ6hIgCAABApHA6pQULvMvtsnBDrzGDCwAAEG7tIwluNws39BEFLgAAQDgFRhLoktBnRBQAAADChUhCSDCDCwAA0N+IJIQUBS4AAEB/IpIQckQUAAAA+ktgJIGFG0KCGVwAAIBQ6yqSQHEbEhS4AAAAocTCDf2OiAIAAECo0CUhLJjBBQAACDa6JIQVBS4AAEAwEUkIOyIKAAAAwUIkISIwgwsAANBXRBIiCgUuAABAX7hc0uTJLNwQQYgoAAAA9JbTKeXlSR4PCzdEEGZwAQAAeqqpSXI4vJEEj8f7UBkLN0SMHhe4+/bt04wZM5SWlqaYmBht27bN73hMTEynr6eeesp3zpVXXtnh+MqVK/v8YQAAAEKuLZKwZo13u7hY2ruXSEIE6XFEobGxUePGjdP999+vWbNmdTh+4sQJv+0dO3YoLy9Ps2fP9tu/YsUKLVy40LedkJDQ06EAAAD0r8BIAl0SIlKPC9zp06dr+gWm3+12u9/2K6+8ottuu01XXXWV3/6EhIQO5wIAAESkpiZp6dLzD5JlZUmbNzNrG6FCmsGtq6vTX/7yF+Xl5XU4tnLlSg0fPlw33nijnnrqKZ09e7bL92lubpbb7fZ7AQAA9IvOFm4gkhDRQtpF4Y9//KMSEhI6RBl+/OMfa/z48UpOTtabb76p4uJinThxQk8//XSn71NSUqJHH300lEMFAADoiIUbolKMYRhGr384JkZbt27VzJkzOz0+ZswYff3rX9ezzz57wff5wx/+oB/84Ac6ffq04uPjOxxvbm5Wc3Ozb9vtdis9PV0NDQ1KTEzs7fABAAA6FxhJmDJF2rSJWdswcrvdstls3ar/QjaD+5//+Z86dOiQXnrppYuem5GRobNnz+rvf/+7vvSlL3U4Hh8f32nhCwAAEHQ1Nd72X1VV3u2iIumxx6SBLB8QLUJ2pX7/+99rwoQJGjdu3EXPra6uVmxsrFJSUkI1HAAAgIsjkmAKPS5wT58+LZfL5ds+cuSIqqurlZycrFGjRknyTiE7nU79+te/7vDz5eXlqqio0G233aaEhASVl5dryZIluueee3TppZf24aMAAAD0EpEEU+lxgfv222/rtttu820XFBRIknJzc7Vu3TpJ0ubNm2UYhubNm9fh5+Pj47V582b94he/UHNzs0aPHq0lS5b43gcAAKBfuVzS3XefjyQUF0srVhBJiGJ9esgsXHoSMgYAAOgSCzdEjZ7UfyHtgwsAABCRmpqk/Hzvw2Qej7fPbXU1xa1JUOACAABrcbmkyZPP522Lili4wWQIlwAAAOsIjCSsXy9Nnx7uUSHImMEFAADmFxhJmDLFG0mguDUlClwAAGBugZGEwkJpzx4iCSZGRAEAAJgXkQRLYgYXAACYD5EES6PABQAA5lJT42371b5LApEESyGiAAAAzMPplBYskNxuIgkWxgwuAACIfu0jCW73+YUbKG4tiQIXAABEt84iCSzcYGlEFAAAQPQikoBOMIMLAACiD5EEXAAFLgAAiC6BkYTCQiIJ8ENEAQAARA8iCegGZnABAEDkC4wksHADLoACFwAARLbASEJxMQs34IKIKAAAgMgVGEnYsEGaNi3co0KEYwYXAABEnq4iCRS36AYKXAAAEFmIJKCPiCgAAIDIQSQBQcAMLgAACD8iCQgiClwAABBeLpc0ebL/wg1EEtAHRBQAAED4OJ1SXp7k8bBwA4KGGVwAAND/mpokh8MbSfB4WLgBQUWBCwAA+ldbJGHNGu82XRIQZEQUAABA/wmMJNAlASHADC4AAAi99l0S2kcSKG4RAhS4AAAgtFi4Af2MiAIAAAidLVu8kQQWbkA/YgYXAAAEX1skYc4cFm5Av6PABQAAwUUkAWFGRAEAAASP0yktWEAkAWHFDC4AAOi75ubzXRKIJCDMKHABAEDfuFxSZiaRBEQMIgoAAKD3AhduWL+e5XYRdszgAgCAnutq4QaKW0QAClwAANAzLpc0efL5SEJREZEERBQiCgAAoPsCuyQQSUAEYgYXAABcXPtIQvsuCRS3iEAUuAAA4MKIJCDKEFEAAABdo0sCohAzuAAAoCO6JCCKUeACAAB/NTVSVhaRBEStHhe4+/bt04wZM5SWlqaYmBht27bN7/i9996rmJgYv9e0gGX6Tp06pfnz5ysxMVFJSUnKy8vT6dOn+/RBAABAEDid0vjxUlWVN5Lw2mtSSYk0kFQjokePC9zGxkaNGzdOz7X9V10npk2bphMnTvhemzZt8js+f/58vffee9q1a5e2b9+uffv2adGiRT0fPQAACI7ALglZWUQSELV6/J9j06dP1/SL3Ozx8fGy2+2dHvvggw+0c+dOHThwQBMnTpQkPfvss7rjjjv0q1/9SmlpaT0dEgAA6IuaGm9hW1Xl3S4qkh57jFlbRK2QZHD37t2rlJQUfelLX9IDDzygzz77zHesvLxcSUlJvuJWkrKzsxUbG6uKiopO36+5uVlut9vvBQAAgoBIAkwo6AXutGnT9Kc//UmlpaV68sknVVZWpunTp+vcuXOSpNraWqWkpPj9zMCBA5WcnKza2tpO37OkpEQ2m833Sk9PD/awAQCwFiIJMLGg/+fZ3Llzff/7+uuv1w033KCrr75ae/fu1dSpU3v1nsXFxSooKPBtu91uilwAAHqLSAJMLuRtwq666iqNGDFCLpdLkmS323Xy5Em/c86ePatTp051mduNj49XYmKi3wsAAPQCkQRYQMgL3I8//lifffaZRo4cKUnKzMxUfX29Kisrfefs3r1bra2tysjICPVwAACwpsBIAgs3wMR6/J9rp0+f9s3GStKRI0dUXV2t5ORkJScn69FHH9Xs2bNlt9tVU1Ojhx56SP/yL/+inJwcSdLYsWM1bdo0LVy4UGvXrlVLS4vy8/M1d+5cOigAABAKgZGE4mJpxQpmbWFaMYZhGD35gb179+q2227rsD83N1fPP/+8Zs6cqYMHD6q+vl5paWm6/fbb9dhjjyk1NdV37qlTp5Sfn69XX31VsbGxmj17tlavXq1hw4Z1awxut1s2m00NDQ3EFQAAuBCnU1qwwDtrO3y4tH49s7aISj2p/3pc4EYCClwAAC6iqUlauvT8crtTpkibNrHcLqJWT+q/kGdwAQBAP6up8bb9aitui4qkPXsobmEZhG8AADATIgkAM7gAAJgCCzcAPhS4AABEO5dLmjzZP5Kwdy+RBFgWEQUAAKKZ0ynl5UkeD5EE4P9jBhcAgGjU1CQ5HN5IgsfDwg1AOxS4AABEm7ZIwpo13m26JAB+iCgAABBNAiMJGzZI06aFe1RARKHARdRpbW3V8ePHlZCQoJiYmHAPB/3MMAx5PB6lpaUpNpYvoWAhLNwAdBsFLrqtpKREL7/8sj788EMNGTJEkydP1pNPPqkvfelLvnOampr005/+VJs3b1Zzc7NycnK0Zs0av6Wajx49qgceeEB79uzRsGHDlJubq5KSEg3s5prox48fV3p6etA/H6LLsWPHdDn/sMMqamq8WduqKu92cbG0YoXUzb+bgNXw/wx0W1lZmRwOh2666SadPXtWP//5z3X77bfr/fff1yWXXCJJWrJkif7yl7/I6XTKZrMpPz9fs2bN0n/9139Jks6dO6c777xTdrtdb775pk6cOKHvf//7GjRokJ544olujSMhIUGSt8BhqWbrcbvdSk9P990HgOkFLtxAJAG4qBjDMIxwD6KnerIWMULnk08+UUpKisrKynTLLbeooaFBl112mTZu3KjvfOc7kqQPP/xQY8eOVXl5uW6++Wbt2LFD3/jGN3T8+HHfrO7atWtVWFioTz75RHFxcRf9vVx/a+P6wzICIwlZWdLmzUQSYFk9+ftPgA291tDQIElKTk6WJFVWVqqlpUXZ2dm+c8aMGaNRo0apvLxcklReXq7rr7/eL7KQk5Mjt9ut9957r9Pf09zcLLfb7feCBdTUSAUFUmtruEcC9L/AhRuKi1m4AegBClz0SmtrqxYvXqysrCxdd911kqTa2lrFxcUpKSnJ79zU1FTV1tb6zmlf3LYdbzvWmZKSEtlsNt+L/K0FbNkijR8v/eY30tNPh3s0QP9yOr33/8GD3kjCjh3SE0+QtwV6gAIXveJwOPTuu+9q8+bNIf9dxcXFamho8L2OHTsW8t+JMGlqkvLzpTlzvHnDrCxp7txwjwroH233f+DCDeRtgR6jwEWP5efna/v27dqzZ4/fU+x2u11nzpxRfX293/l1dXWy2+2+c+rq6jocbzvWmfj4eCUmJvq9YEI1Nd6Ctu0r2cJCvpKFdQRGEgoLWbgB6AMKXHSbYRjKz8/X1q1btXv3bo0ePdrv+IQJEzRo0CCVlpb69h06dEhHjx5VZmamJCkzM1N//etfdfLkSd85u3btUmJioq699tr++SCIPG2RhKoq71eyr70mrVzJV7KwhsBIAvc/0Gf8vwfd5nA4tHHjRr3yyitKSEjwZWZtNpuGDBkim82mvLw8FRQUKDk5WYmJiXrwwQeVmZmpm2++WZJ0++2369prr9X3vvc9rVq1SrW1tXr44YflcDgUHx8fzo+HcOApcVgZCzcAIUOBi257/vnnJUm33nqr3/4XXnhB9957ryTpN7/5jWJjYzV79my/hR7aDBgwQNu3b9cDDzygzMxMXXLJJcrNzdWKFSv662MgUgQ2ri8slB5/nFkrWIPLJd199/n7v6hIeuwx7n8gSOiDi6jD9TeBwMb169dL06d360e5/oh6TqeUl+d9kKyH9z9gZfTBBRCZmpokh8M7c+t2n39KnH/cYQWBXRKysrj/gRChwAXQP9qeEm+LrBQX85Q4rCOwS0JREV1CgBAi7AMg9LZs8X4l2xZJ2LCB3p6wDiIJQL9jBhdA6DQ3+y/cQON6WElXCzdQ3AIhR4ELIDRcLikz0/8rWSIJsIrOFi7h/gf6DREFAMHXhy4JQNTj/gfCjhlcAMHTFklo65LAU+KwkvaRBO5/IKwocAEER+BT4oWFPCUO6wiMJNAlAQgrIgoA+o6nxGFlRBKAiMMMLoDeax9J4ClxWE1gJIH7H4gYFLgAeqempmPjep4Sh1UERhJYuASIKEQUAPRc+69kR4yQ/vQnZq1gHSxcAkQ8ZnABdF9nX8kePEhxC2tou/9ZuASIeBS4ALqns6fE+UoWVsH9D0QVIgoALo4uCbCywC4JRBKAiMcMLoCutY8keDw0roe1dNUlgeIWiHgUuAA6R+N6WBmRBCCqEVEA0BGN62Fl3P9A1GMGF8B5TU2Sw0HjelhTYCSBSA4QtShwAXi1LdywZo13m69kYSUuV8eFS4jkAFGLiAIAvpKFtdElBDAdZnABK2tu7vwpcf5xhxUEdgnh/gdMgwIXsKq2SELbV7KFhUQSYB10SQBMjYgCYEVEEmBlLNwAmF6PZ3D37dunGTNmKC0tTTExMdq2bZvvWEtLiwoLC3X99dfrkksuUVpamr7//e/r+PHjfu9x5ZVXKiYmxu+1cuXKPn8YABfRVeN6iltYQVeRHIpbwHR6XOA2NjZq3Lhxeq7ta512Pv/8c1VVVWnZsmWqqqrSyy+/rEOHDumb3/xmh3NXrFihEydO+F4PPvhg7z4BgO7hK1lYWWAkp7iY+x8wsR5HFKZPn67pXcz22Gw27dq1y2/fb3/7W02aNElHjx7VqFGjfPsTEhJkt9t7+usB9AaRBFgZ9z9gOSF/yKyhoUExMTFKSkry279y5UoNHz5cN954o5566imdPXu2y/dobm6W2+32ewHohsCFG2hcDyshkgNYVkgfMmtqalJhYaHmzZunxMRE3/4f//jHGj9+vJKTk/Xmm2+quLhYJ06c0NNPP93p+5SUlOjRRx8N5VAB86mp8f7DXlXl3S4ullaskAbybCkswOXy3v8HD3q3uf8BS4kxDMPo9Q/HxGjr1q2aOXNmh2MtLS2aPXu2Pv74Y+3du9evwA30hz/8QT/4wQ90+vRpxcfHdzje3Nys5uZm37bb7VZ6eroaGhou+L4wJ7fbLZvNxvW/kC1bvI3rTfiVLNcfFxW4cANdEgBT6Mnf/5BEFFpaWnTXXXfpo48+0q5duy46iIyMDJ09e1Z///vfOz0eHx+vxMREvxeATrR9JTtnDpEEWE9XCzdQ3AKWE/QCt624PXz4sF5//XUNHz78oj9TXV2t2NhYpaSkBHs4gHV01iVh716eEoc1uFz+XRLoEgJYWo/DSKdPn5bL5fJtHzlyRNXV1UpOTtbIkSP1ne98R1VVVdq+fbvOnTun2tpaSVJycrLi4uJUXl6uiooK3XbbbUpISFB5ebmWLFmie+65R5deemnwPhlgJTwlDisLjCRw/wMwemjPnj2GpA6v3Nxc48iRI50ek2Ts2bPHMAzDqKysNDIyMgybzWYMHjzYGDt2rPHEE08YTU1N3R5DQ0ODIcloaGjo6fDRR2VlZcY3vvENY+TIkYYkY+vWrX7HW1tbjWXLlhl2u90YPHiwMXXqVONvf/ub3zmfffaZ8d3vftdISEgwbDabcf/99xsej6fbY+D6t/PPfxqGw2EYkveVlWUYx46Fe1QhxfWHT+D9P2WK6e9/wMp68ve/xzO4t956q4wLPJd2oWOSNH78eO3fv7+nvxYRom2hj/vvv1+zZs3qcHzVqlVavXq1/vjHP2r06NFatmyZcnJy9P7772vw4MGSpPnz5+vEiRPatWuXWlpadN9992nRokXauHFjf3+c6OZySXfffb5LQlGR9NhjPCUOawjsksD9D6CdPnVRCBeeoo4MgV00DMNQWlqafvrTn2rp0qWSvH2QU1NTtW7dOs2dO1cffPCBrr32Wh04cEATJ06UJO3cuVN33HGHPv74Y6WlpV3093L9ZelIAtcfRBIAawp7FwVY05EjR1RbW6vs7GzfPpvNpoyMDJWXl0uSysvLlZSU5CtuJSk7O1uxsbGqqKjo9H1Z6KOd5mYa18O6uuqSwP0PIAAFLoKm7YHC1NRUv/2pqam+Y7W1tR26ZQwcOFDJycm+cwKVlJTIZrP5Xunp6SEYfRSoqeEpcVgXXRIA9AAFLiJecXGxGhoafK9jx46Fe0j9b8sWafx4b952+HDptdekkhLyhrAGp1OaMMGbt+X+B9ANFLgIGrvdLkmqq6vz219XV+c7ZrfbdfLkSb/jZ8+e1alTp3znBLL0Qh8s3AArax9J4P4H0AMUuAia0aNHy263q7S01LfP7XaroqJCmZmZkqTMzEzV19ersrLSd87u3bvV2tqqjIyMfh9zRHO5WLgB1sXCJQD6gO930CMXWuhj1KhRWrx4sR5//HFdc801vjZhaWlpvk4LY8eO1bRp07Rw4UKtXbtWLS0tys/P19y5c7vVQcEyLNwlAeD+B9BXFLjokbffflu33Xabb7ugoECSlJubq3Xr1umhhx5SY2OjFi1apPr6ek2ZMkU7d+709cCVpBdffFH5+fmaOnWqYmNjNXv2bK1evbrfP0tEamqSli49P2uVlSVt3sysFayB+x9AkNAHF1HHtNe/psabNWThhgsy7fW3Ou5/ABfRk7///OUAIkHgV7IbNkjTpoV7VED/IJIAIMh4yAwIp8CnxNsa11Pcwgq6uv8pbgH0EQUuEC6dPSVO43pYReD9X1zM/Q8gaIgoAOGwZYuUl8dXsrAmIjkAQowZXKA/sXADrIxIDoB+QoEL9JfOvpKlcT2sgkgCgH5ERAHoDzwlDisjkgCgnzGDC4QST4nDyogkAAgTClwgVFwuafJkuiTAmrj/AYQREQUgFJxOb5cEj4dIAqyH+x9AmDGDCwRTU5PkcHi/kvV4iCTAWgLvf7qEAAgTClwgWFwu7z/oa9Z4t3lKHFbSFklof//TJQRAmBBRAIKBLgmwssBIAl0SAIQZM7hAXwQ+Jc5XsrCS9vd/+0gOxS2AMKPABXqrLZLQ9pR4YSFfycI6WLgBQAQjogD0Bk+Jw8pYuAFAhGMGF+iJwK9kiSTASli4AUCUoMAFuivwK9nCQr6ShXUE3v8s3AAgghFRALqDSAKsjEgCgCjDDC5wIc3NnT8lTnELKyCSACBKUeACXamp8Tau5ytZWFFglxC6JACIIkQUgM6wcAOsjIUbAEQ5ZnCB9tpHEli4AVbTVZcQilsAUYYCF2jjcnWMJLBwA6yisy4J3P8AohQRBUCiSwKsjfsfgMkwgwtrY+EGWFng/U+XEAAmQYEL6yKSACtj4QYAJkZEAdZElwRY2ZYt3kgC9z8Ak2IGF9YS2LieSAKspO3+nzPHf+EG7n8AJkOBC+vgKXFYGZEEABZCRAHWwFeysDIiOQAshhlcmFvgV7JEEmAlRHIAWBQFLsyLSAKsLPD+Lyzk/gdgGRS4CIvnnntOV155pQYPHqyMjAy99dZbwf0FTqc0frxUVeX9Sva116SSEmkgqRxYQGf3/8qV3P8ALIMCF/3upZdeUkFBgZYvX66qqiqNGzdOOTk5OnnyZN/fvKlJcjjOfyXLU+KwEiIJACCpFwXuvn37NGPGDKWlpSkmJkbbtm3zO24Yhh555BGNHDlSQ4YMUXZ2tg4fPux3zqlTpzR//nwlJiYqKSlJeXl5On36dJ8+CKLH008/rYULF+q+++7Ttddeq7Vr12ro0KH6wx/+0Lc3bvtKds0a73ZxMU+JwzqIJACAT48L3MbGRo0bN07Ptf0RDbBq1SqtXr1aa9euVUVFhS655BLl5OSoqanJd878+fP13nvvadeuXdq+fbv27dunRYsW9f5TIGqcOXNGlZWVys7O9u2LjY1Vdna2ysvLO/2Z5uZmud1uv1cH+/Z1/Er2iSf4ShbWQCQBAPz0+K/f9OnTNb2Lr7sMw9Azzzyjhx9+WN/61rckSX/605+Umpqqbdu2ae7cufrggw+0c+dOHThwQBMnTpQkPfvss7rjjjv0q1/9SmlpaX34OIh0n376qc6dO6fU1FS//ampqfrwww87/ZmSkhI9+uijF37j666TkpKkG26QNm1i1grW0NQkLV16ftZ2yhTufwBQkDO4R44cUW1trd/snM1mU0ZGhm92rry8XElJSb7iVpKys7MVGxurioqKTt+3WzN4MK3i4mI1NDT4XseOHet4UnKy9+tYIgmwisBIApEcAPAJ6vdXtbW1ktTp7FzbsdraWqWkpPgPYuBAJScn+84J1K0ZPESFESNGaMCAAaqrq/PbX1dXJ7vd3unPxMfHKz4+/uJvPnp0MIYIRL7AhRs2bJCmTQv3qAAgYkRFQKu4uFgFBQW+bbfbrfT09DCOCL0VFxenCRMmqLS0VDNnzpQktba2qrS0VPn5+d16D8MwJImZfItqu+5t94GlEEkAgG4JaoHbNgNXV1enkSNH+vbX1dXpK1/5iu+cwHZQZ8+e1alTp/o+g4eoUFBQoNzcXE2cOFGTJk3SM888o8bGRt13333d+nmPxyNJ/EeOxXk8HtlstnAPo//U1Hjbf1VVebeLi6UVK3iQDAA6EdS/jKNHj5bdbldpaamvoHW73aqoqNADDzwgScrMzFR9fb0qKys1YcIESdLu3bvV2tqqjIyMYA4HEeruu+/WJ598okceeUS1tbX6yle+op07d3aItnQlLS1Nx44dU0JCgmJiYnz722b2jx07psTExFANPyJY5bN29jkNw5DH47HWA6lEEgCgR3pc4J4+fVoul8u3feTIEVVXVys5OVmjRo3S4sWL9fjjj+uaa67R6NGjtWzZMqWlpfm+jh47dqymTZumhQsXau3atWppaVF+fr7mzp1rrX+wLC4/P7/bkYRAsbGxuvwCX8kmJiaauuhrzyqfNfBzWmbmlkgCAPRKjwvct99+W7fddptvuy0bm5ubq3Xr1umhhx5SY2OjFi1apPr6ek2ZMkU7d+7U4MGDfT/z4osvKj8/X1OnTlVsbKxmz56t1atXB+HjAIBJuFzeSMLBg97twkLp8ceJJABAN8QYUfikhtvtls1mU0NDgyVmr9A9VrovrPJZrfI5O3A6pbw8yePxRhLWr2e5XQCW15N/E4LaBxcIp/j4eC1fvtwSDyRa5bNa5XP6NDVJDod35tbj8UYSqqspbgGgh5jBBYBIEBhJoEsCAPjpSf3HX04ACLfASAJdEgCgT4goAEC4NDVJ+fkdIwkUtwDQJxS4ABAONTVSVtb5FmDFxdKePbQAA4AgIKIAAP1tyxZvJIGFGwAgJJjBBYD+0hZJmDPHW9wSSQCAkKDAhSk899xzuvLKKzV48GBlZGTorbfeCveQ+uwXv/iFYmJi/F5jxozxHW9qapLD4dDw4cM1bNgwzZ49W3V1dWEccfft27dPM2bMUFpammJiYrRt2za/44Zh6JFHHtHIkSM1ZMgQZWdn6/Dhw37nnDp1SvPnz1diYqKSkpKUl5en06dP9+On6CEiCQDQbyhwEfVeeuklFRQUaPny5aqqqtK4ceOUk5OjkydPhntoffblL39ZJ06c8L3eeOMN37ElS5bo1VdfldPpVFlZmY4fP65Zs2aFcbTd19jYqHHjxum5tmIvwKpVq7R69WqtXbtWFRUVuuSSS5STk6OmpibfOfPnz9d7772nXbt2afv27dq3b58WLVrUXx+hZ5xOafx4qarKG0nYsUN64glagAFAqBhRqKGhwZBkNDQ0hHsoiACTJk0yHA6Hb/vcuXNGWlqaUVJSEsZR9d3y5cuNcePGdXqsvr7eGDRokOF0On37PvjgA0OSUV5e3k8jDA5JxtatW33bra2tht1uN5566infvvr6eiM+Pt7YtGmTYRiG8f777xuSjAMHDvjO2bFjhxETE2P84x//6LexX1RTk2E4HIYheV9TphjGsWPhHhUARKWe1H/M4CKqnTlzRpWVlcrOzvbti42NVXZ2tsrLy8M4suA4fPiw0tLSdNVVV2n+/Pk6evSoJKmyslItLS1+n3vMmDEaNWpU1H/uI0eOqLa21u+z2Ww2ZWRk+D5beXm5kpKSNHHiRN852dnZio2NVUVFRb+PuVMul5SZSSQBAMKAAhdR7dNPP9W5c+eUmprqtz81NVW1tbVhGlVwZGRkaN26ddq5c6eef/55HTlyRF/96lfl8XhUW1uruLg4JSUl+f2MGT532/gvdE1ra2uVkpLid3zgwIFKTk6OjM/fFkk4eNAbSXjtNSIJANCP+GsLRKjp06f7/vcNN9ygjIwMXXHFFfrzn/+sIUOGhHFk6FJTk7R06flZ2ylTpE2bmLUFgH7GDC6i2ogRIzRgwIAO3QPq6upkt9vDNKrQSEpK0he/+EW5XC7Z7XadOXNG9fX1fueY4XO3jf9C19Rut3d4iPDs2bM6depU+D6/yyVNnny+uC0qIpIAAGFCgYuoFhcXpwkTJqi0tNS3r7W1VaWlpcrMzAzjyILv9OnTqqmp0ciRIzVhwgQNGjTI73MfOnRIR48ejfrPPXr0aNntdr/P5na7VVFR4ftsmZmZqq+vV2Vlpe+c3bt3q7W1VRkZGf0+Zjmd0oQJ/pGEkhIiCQAQJvz1RdQrKChQbm6uJk6cqEmTJumZZ55RY2Oj7rvvvnAPrU+WLl2qGTNm6IorrtDx48e1fPlyDRgwQPPmzZPNZlNeXp4KCgqUnJysxMREPfjgg8rMzNTNN98c7qFf1OnTp+VyuXzbR44cUXV1tZKTkzVq1CgtXrxYjz/+uK655hqNHj1ay5YtU1pammbOnClJGjt2rKZNm6aFCxdq7dq1amlpUX5+vubOnau0tLT++yBEEgAgMvVDV4ego00YAj377LPGqFGjjLi4OGPSpEnG/v37wz2kPrv77ruNkSNHGnFxccYXvvAF4+677zZcLpfv+D//+U/jRz/6kXHppZcaQ4cONb797W8bJ06cCOOIu2/Pnj2GpA6v3NxcwzC8rcKWLVtmpKamGvHx8cbUqVONQ4cO+b3HZ599ZsybN88YNmyYkZiYaNx3332Gx+Ppvw9x+LBh3Hjj+RZgRUWG0dLSf78fACymJ/VfjGEYRhjr615xu92y2WxqaGhQYmJiuIcDwGqcTikvT/J4vJGE9euldg8FAgCCryf1HxlcAOiupiYpP1+66y5vcTtlilRdTXELABGGAhcAuqOmRsrKoksCAEQBHjIDgItxOqUFCyS3m0gCAEQBZnABoCvtIwlut3cGl0gCAEQ8ClwA6ExnkYS9e4kkAEAUIKIAAIGIJABAVGMGFwDaEEkAAFOgwAUAiUgCAJgIEQUAIJIAAKbCDC4A6wqMJLBwAwCYAgUuAGsKjCQUF7NwAwCYBBEFANZDJAEATI0ZXADWQSQBACyBAheANXTWJYFIAgCYEhEFAOZHJAEALIUZXADmxcINAGBJFLgAzMnlkiZPZuEGALAgIgoAzMfplPLyJI+HSAIAWBAzuADMo6lJcji8kQSPhy4JAGBRFLgAzKEtkrBmjXebLgkAYFlEFABEv8BIwoYN0rRp4R4VACBMmMEFEL3ad0loH0mguAUAS6PABRCdAhduKC4mkgAAkEREAUA0Cly4gUgCAKAdZnABRI+uFm6guAUAtEOBCyA6BC7cUFzMwg0AgE4FvcC98sorFRMT0+HlcDgkSbfeemuHYz/84Q+DPQwAZuJ0SuPHSwcPeiMJO3ZITzwhDSRlBQDoKOj/Ohw4cEDnzp3zbb/77rv6+te/rjlz5vj2LVy4UCtWrPBtDx06NNjDAGAGTU3S0qXnZ22nTJE2bWLWFgBwQUEvcC+77DK/7ZUrV+rqq6/W1772Nd++oUOHym63B/tXAzATl8ubtT140LtdWCg9/jiztgCAiwppBvfMmTPasGGD7r//fsXExPj2v/jiixoxYoSuu+46FRcX6/PPP7/g+zQ3N8vtdvu9AJhYYCThtdeklSspbgEA3RLSfy22bdum+vp63Xvvvb593/3ud3XFFVcoLS1N77zzjgoLC3Xo0CG9/PLLXb5PSUmJHn300VAOFUAkIJIAAAiCGMMwjFC9eU5OjuLi4vTqq692ec7u3bs1depUuVwuXX311Z2e09zcrObmZt+22+1Wenq6GhoalJiYGPRxAwgDl0u6+26pqsq7XVQkPfYYs7YAAEne+s9ms3Wr/gvZvxwfffSRXn/99QvOzEpSRkaGJF2wwI2Pj1d8fHzQxwggQjidUl6ed7nd4cOl9eul6dPDPSoAQJQKWQb3hRdeUEpKiu68884LnlddXS1JGjlyZKiGAiBStV+4weM5v3ADxS0AoA9CMoPb2tqqF154Qbm5uRrY7uvFmpoabdy4UXfccYeGDx+ud955R0uWLNEtt9yiG264IRRDARCpArskEEkAAARJSP4lef3113X06FHdf//9fvvj4uL0+uuv65lnnlFjY6PS09M1e/ZsPfzww6EYBoBIRSQBABBCIX3ILFR6EjIGEEHokgAA6KWe1H8h7YMLAD41Nd6MbVtxW1go7dlDcQsACDrCbgBCz+mUFiyQ3G4iCQCAkGMGF0DotO+S4HbTJQEA0C8ocAGERmAkoahI2ruXSAIAIOSIKAAIPiIJAIAwYgYXQPAQSQAARAAKXADBERhJKC4mkgAACAsiCgD6bssW78INbZGEDRukadPCPSoAgEUxgwug99oiCXPmeIvbKVO8kQSKWwBAGFHgAuidzroksHADACACEFEA0HOBXRKIJAAAIggzuAC6L7BLApEEAEAEosAF0D1EEgAAUYKIAoCLY+EGAEAUYQYXQNdYuAEAEIUocAF0zuWSJk/2jySwcAMAIAoQUQDQkdPpXbjB4yGSAACIOszgAjivfSTB4znfJYHiFgAQRShwAXjRJQEAYBJEFACwcAMAwFSYwQWsrLmZhRsAAKZDgQtYVU2Nf5eE4mIiCQAAUyCiAFgRCzcAAEyMGVzASgIXbqBLAgDAhChwAasIXLiBSAIAwKSIKABWELhwA10SAAAmxgwuYGZdLdxAcQsAMDEKXMCsAiMJLNwAALAIIgqAGQVGEuiSAACwEGZwATNpv3BD+0gCxS0AwEIocAGzcLmkzEwiCQAAyyOiAJgBkQQAAHyYwQWiWVddEihuAQAWRoELRCsWbgAAoFNEFIBo5HRKCxZ4l9sdMcIbSaC3LQAAkpjBBaJL+0iC2+2NJBw8SHELAEA7FLhAtKipkbKyiCQAAHARRBSAaNA+kjB8uLRhA7O2AAB0gRlcIJJ1Fkmorqa4BQDgAihwgUgVGElg4QYAALqFiAIQiQIjCSzcAABAtzGDC0SSriIJFLcAAHQbBS4QKeiSAABAUBBRACIBXRIAAAiaoM/g/uIXv1BMTIzfa8yYMb7jTU1NcjgcGj58uIYNG6bZs2errq4u2MMAogNdEgAACLqQRBS+/OUv68SJE77XG2+84Tu2ZMkSvfrqq3I6nSorK9Px48c1a9asUAwDiGxEEgAACImQRBQGDhwou93eYX9DQ4N+//vfa+PGjfrXf/1XSdILL7ygsWPHav/+/br55ptDMRwg8hBJAAAgZEIyg3v48GGlpaXpqquu0vz583X06FFJUmVlpVpaWpSdne07d8yYMRo1apTKy8u7fL/m5ma53W6/FxCViCQAABByQS9wMzIytG7dOu3cuVPPP/+8jhw5oq9+9avyeDyqra1VXFyckpKS/H4mNTVVtbW1Xb5nSUmJbDab75Wenh7sYQOh53JJkyezcAMAACEW9IjC9Hb9Om+44QZlZGToiiuu0J///GcNGTKkV+9ZXFysgoIC37bb7abIRXRxOqW8PMnjYeEGAABCLOR9cJOSkvTFL35RLpdLdrtdZ86cUX19vd85dXV1nWZ228THxysxMdHvBUSFpibJ4fBGEjwe70NlLNwAAEBIhbzAPX36tGpqajRy5EhNmDBBgwYNUmlpqe/4oUOHdPToUWVmZoZ6KED/aoskrFnj3S4qkvbuJZIAAECIBT2isHTpUs2YMUNXXHGFjh8/ruXLl2vAgAGaN2+ebDab8vLyVFBQoOTkZCUmJurBBx9UZmYmHRRgLkQSAAAIm6AXuB9//LHmzZunzz77TJdddpmmTJmi/fv367LLLpMk/eY3v1FsbKxmz56t5uZm5eTkaE3bDBcQ7ZqapKVLzz9INmWKtGkTs7YAAPSjGMMwjHAPoqfcbrdsNpsaGhrI4yJy1NR4s7ZVVd7t4mJpxQppICtiAwDQVz2p//iXFwgGFm4AACBihPwhM8DUWLgBAICIQ4EL9FZNjbftFws3AAAQUYgoAL1BJAEAgIjFDC7QE0QSAACIeBS4QHe5XP6RhOJiIgkAAEQgIgpAdwQu3EAkAQCAiMUMLnAh7SMJHo93BpdIAgAAEY0CF+iKyyVNnuzfJWHvXiIJAABEOCIKQGcCIwnr10vTp4d7VAAAoBuYwQXaC4wktHVJoLgFACBqUOACbQIjCYWFdEkAACAKEVEAJLokAABgIszgwtq6iiRQ3AIAELUocGFdNTUs3AAAgAkRUYA1OZ3SggXe5XZHjPB2SWDWFgAAU2AGF9bSPpLgdnsjCQcPUtwCAGAiFLiwDiIJAABYAhEFWEP7SAJdEgAAMDVmcGFunUUS6JIAAICpUeDCvAIjCSzcAACAJRBRgDkFRhLWr2e5XQAALIIZXJhLV5EEilsAACyDAhfmQZcEAAAgIgowC7okAACA/48ZXEQ3uiQAAIAAFLiIXkQSAABAJ4goIDoRSQAAAF1gBhfRhUgCAAC4CApcRA+XS5o8mYUbAADABRFRQHRwOqW8PMnjYeEGAABwQczgIrI1NUkOhzeS4PGwcAMAALgoClxErrZIwpo13m26JAAAgG4gooDIFBhJoEsCAADoJmZwEVnad0loH0mguAUAAN1EgYvIwcINAAAgCIgoIDJs2eKNJLBwAwAA6CNmcBFebZGEOXNYuAEAAAQFBS7CJzCSUFREJAEAAPQZEQWEh9MpLVhAJAEAAAQdM7joX83N57skEEkAAAAhQIGL/uNySZmZdEkAAAAhRUQB/SNw4Yb161luFwAAhAQzuAitrhZuoLgFAAAhQoGL0HG5pMmT6ZIAAAD6VdAL3JKSEt10001KSEhQSkqKZs6cqUOHDvmdc+uttyomJsbv9cMf/jDYQ0E4OZ3S+PHSwYPeSMJrr0klJdJAUjEAACC0gl7glpWVyeFwaP/+/dq1a5daWlp0++23q7Gx0e+8hQsX6sSJE77XqlWrgj0UhAORBAAAEGZBn07buXOn3/a6deuUkpKiyspK3XLLLb79Q4cOld1u79Z7Njc3q7m52bftdruDM1gEl8vlLWwPHvRuFxdLK1YwawsAAPpVyDO4DQ0NkqTk5GS//S+++KJGjBih6667TsXFxfr888+7fI+SkhLZbDbfKz09PaRjRi8ERhJ27JCeeILiFgAA9LsYwzCMUL15a2urvvnNb6q+vl5vvPGGb//vfvc7XXHFFUpLS9M777yjwsJCTZo0SS+//HKn79PZDG56eroaGhqUmJgYquGjO5qapKVLzz9INmWKtGkTD5IBAICgcrvdstls3ar/Qjq95nA49O677/oVt5K0aNEi3/++/vrrNXLkSE2dOlU1NTW6+uqrO7xPfHy84uPjQzlU9EZNjTeSUFXl3SaSAAAAIkDIIgr5+fnavn279uzZo8svMpuXkZEhSXK5XKEaDoKtLZJQVSWNGOHtkkAkAQAARICgF7iGYSg/P19bt27V7t27NXr06Iv+THV1tSRp5MiRwR4Ogq19lwS32xtJOHiQLgkAACBiBH26zeFwaOPGjXrllVeUkJCg2tpaSZLNZtOQIUNUU1OjjRs36o477tDw4cP1zjvvaMmSJbrlllt0ww03BHs4CCYiCQAAIAoE/SGzmJiYTve/8MILuvfee3Xs2DHdc889evfdd9XY2Kj09HR9+9vf1sMPP9ztB8Z6EjJGkDid0oIF3lnb4cOlDRukadPCPSoAAGARYX3I7GL1cnp6usrKyoL9axEqgV0SsrKkzZvpkgAAACJWyPvgIorV1HgL2rbitqhI2ruX4hYAAEQ0wpPoXGAkYf16HiQDAABRgRlc+OusS0J1NcUtAACIGhS4OC8wklBcLO3ZQyQBAABEFSIK8CKSAAAATIIZXKsjkgAAAEyGAtfKOuuSQCQBAABEOSIKVkUkAQAAmBQzuFYTGEnIyiKSAAAATIUC10pcLmnyZBZuAAAApkZEwSqcTikvT/J4iCQAAABTYwbX7JqaJIfDG0nweOiSAAAATI8C18zaIglr1ni3WbgBAABYABEFsyKSAAAALIoZXLNp3yWBSAIAALAgClwzCVy4gUgCAACwICIKZhG4cMOGDdK0aeEeFQAAQL9jBjfadbVwA8UtAACwKArcaBa4cENxMQs3AAAAyyOiEK0CuyQQSQAAAJDEDG706apLAsUtAACAJArc6BIYSSgspEsCAABAACIK0YKFGwAAALqFGdxIx8INAAAAPUKBG8lcLv+FG4qKiCQAAABcBBGFSEUkAQAAoFeYwY00gZGEtoUbKG4BAAC6hQI3krBwAwAAQJ8RUYgULNwAAAAQFMzghhsLNwAAAAQVBW441dTQJQEAACDIiCiEi9MpLVggud1EEgAAAIKIGdz+1j6S4HYTSQAAAAgyCtz+RCQBAAAg5Igo9JfASAILNwAAAIQEM7ihFhhJYOEGAACAkKLADaXASAILNwAAAIQcEYVQ2bLFu3ADXRIAAAD6FTO4wdYWSZgzhy4JAAAAYUCBG0x0SQAAAAg7IgrBwsINAAAAEYEZ3L5i4QYAAICIQoHbF0QSAAAAIg4Rhd5i4QYAAICIFLYZ3Oeee05XXnmlBg8erIyMDL311lvhGkrPsHADAABARAtLgfvSSy+poKBAy5cvV1VVlcaNG6ecnBydPHkyHMPpPpdLmjzZP5LAwg0AAAARJcYwDKO/f2lGRoZuuukm/fa3v5Uktba2Kj09XQ8++KCKioo6nN/c3Kzm5mbfttvtVnp6uhoaGpSYmNg/g3Y6vQs3eDxEEgAAAPqZ2+2WzWbrVv3X7zO4Z86cUWVlpbKzs88PIjZW2dnZKi8v7/RnSkpKZLPZfK/09PT+Gq7Xp5+eL27buiRQ3AIAAESkfi9wP/30U507d06pqal++1NTU1VbW9vpzxQXF6uhocH3OnbsWH8M9bwRI6T/+3/pkgAAABAFoqKLQnx8vOLj48M7iLvu8r4AAAAQ0fp9BnfEiBEaMGCA6urq/PbX1dXJbrf393AAAABgMv1e4MbFxWnChAkqLS317WttbVVpaakyMzP7ezgAAAAwmbBEFAoKCpSbm6uJEydq0qRJeuaZZ9TY2Kj77rsvHMMBAACAiYSlwL377rv1ySef6JFHHlFtba2+8pWvaOfOnR0ePAMAAAB6Kix9cPuqJ33QAAAAEP0iug8uAAAAEEoUuAAAADAVClwAAACYCgUuAAAATIUCFwAAAKZCgQsAAABTocAFAACAqVDgAgAAwFQocAEAAGAqFLgAAAAwFQpcAAAAmAoFLgAAAEyFAhcAAACmMjDcA+gNwzAkSW63O8wjAQAAQH9oq/va6sALicoC1+PxSJLS09PDPBIAAAD0J4/HI5vNdsFzYozulMERprW1VcePH1dCQoJiYmL65Xe63W6lp6fr2LFjSkxM7JffidDjupoP19ScuK7mwzU1p1BeV8Mw5PF4lJaWptjYC6dso3IGNzY2VpdffnlYfndiYiL/RzQhrqv5cE3NietqPlxTcwrVdb3YzG0bHjIDAACAqVDgAgAAwFQocLspPj5ey5cvV3x8fLiHgiDiupoP19ScuK7mwzU1p0i5rlH5kBkAAADQFWZwAQAAYCoUuAAAADAVClwAAACYCgUuAAAATIUCFwAAAKZCgdtNzz33nK688koNHjxYGRkZeuutt8I9JHRTSUmJbrrpJiUkJCglJUUzZ87UoUOH/M5pamqSw+HQ8OHDNWzYMM2ePVt1dXVhGjF6auXKlYqJidHixYt9+7im0ekf//iH7rnnHg0fPlxDhgzR9ddfr7ffftt33DAMPfLIIxo5cqSGDBmi7OxsHT58OIwjxsWcO3dOy5Yt0+jRozVkyBBdffXVeuyxx9S+iRPXNbLt27dPM2bMUFpammJiYrRt2za/4925fqdOndL8+fOVmJiopKQk5eXl6fTp0yEbMwVuN7z00ksqKCjQ8uXLVVVVpXHjxiknJ0cnT54M99DQDWVlZXI4HNq/f7927dqllpYW3X777WpsbPSds2TJEr366qtyOp0qKyvT8ePHNWvWrDCOGt114MAB/du//ZtuuOEGv/1c0+jzv//7v8rKytKgQYO0Y8cOvf/++/r1r3+tSy+91HfOqlWrtHr1aq1du1YVFRW65JJLlJOTo6ampjCOHBfy5JNP6vnnn9dvf/tbffDBB3ryySe1atUqPfvss75zuK6RrbGxUePGjdNzzz3X6fHuXL/58+frvffe065du7R9+3bt27dPixYtCt2gDVzUpEmTDIfD4ds+d+6ckZaWZpSUlIRxVOitkydPGpKMsrIywzAMo76+3hg0aJDhdDp953zwwQeGJKO8vDxcw0Q3eDwe45prrjF27dplfO1rXzN+8pOfGIbBNY1WhYWFxpQpU7o83traatjtduOpp57y7auvrzfi4+ONTZs29ccQ0Qt33nmncf/99/vtmzVrljF//nzDMLiu0UaSsXXrVt92d67f+++/b0gyDhw44Dtnx44dRkxMjPGPf/wjJONkBvcizpw5o8rKSmVnZ/v2xcbGKjs7W+Xl5WEcGXqroaFBkpScnCxJqqysVEtLi981HjNmjEaNGsU1jnAOh0N33nmn37WTuKbR6j/+4z80ceJEzZkzRykpKbrxxhv17//+777jR44cUW1trd91tdlsysjI4LpGsMmTJ6u0tFR/+9vfJEn//d//rTfeeEPTp0+XxHWNdt25fuXl5UpKStLEiRN952RnZys2NlYVFRUhGdfAkLyriXz66ac6d+6cUlNT/fanpqbqww8/DNOo0Futra1avHixsrKydN1110mSamtrFRcXp6SkJL9zU1NTVVtbG4ZRojs2b96sqqoqHThwoMMxrml0+p//+R89//zzKigo0M9//nMdOHBAP/7xjxUXF6fc3Fzftevs7zHXNXIVFRXJ7XZrzJgxGjBggM6dO6df/vKXmj9/viRxXaNcd65fbW2tUlJS/I4PHDhQycnJIbvGFLiwFIfDoXfffVdvvPFGuIeCPjh27Jh+8pOfaNeuXRo8eHC4h4MgaW1t1cSJE/XEE09Ikm688Ua9++67Wrt2rXJzc8M8OvTWn//8Z7344ovauHGjvvzlL6u6ulqLFy9WWloa1xUhQ0ThIkaMGKEBAwZ0ePq6rq5Odrs9TKNCb+Tn52v79u3as2ePLr/8ct9+u92uM2fOqL6+3u98rnHkqqys1MmTJzV+/HgNHDhQAwcOVFlZmVavXq2BAwcqNTWVaxqFRo4cqWuvvdZv39ixY3X06FFJ8l07/h5Hl5/97GcqKirS3Llzdf311+t73/uelixZopKSEklc12jXnetnt9s7PJh/9uxZnTp1KmTXmAL3IuLi4jRhwgSVlpb69rW2tqq0tFSZmZlhHBm6yzAM5efna+vWrdq9e7dGjx7td3zChAkaNGiQ3zU+dOiQjh49yjWOUFOnTtVf//pXVVdX+14TJ07U/Pnzff+baxp9srKyOrTw+9vf/qYrrrhCkjR69GjZ7Xa/6+p2u1VRUcF1jWCff/65YmP9y40BAwaotbVVEtc12nXn+mVmZqq+vl6VlZW+c3bv3q3W1lZlZGSEZmAheXTNZDZv3mzEx8cb69atM95//31j0aJFRlJSklFbWxvuoaEbHnjgAcNmsxl79+41Tpw44Xt9/vnnvnN++MMfGqNGjTJ2795tvP3220ZmZqaRmZkZxlGjp9p3UTAMrmk0euutt4yBAwcav/zlL43Dhw8bL774ojF06FBjw4YNvnNWrlxpJCUlGa+88orxzjvvGN/61reM0aNHG//85z/DOHJcSG5urvGFL3zB2L59u3HkyBHj5ZdfNkaMGGE89NBDvnO4rpHN4/EYBw8eNA4ePGhIMp5++mnj4MGDxkcffWQYRveu37Rp04wbb7zRqKioMN544w3jmmuuMebNmxeyMVPgdtOzzz5rjBo1yoiLizMmTZpk7N+/P9xDQjdJ6vT1wgsv+M755z//afzoRz8yLr30UmPo0KHGt7/9bePEiRPhGzR6LLDA5ZpGp1dffdW47rrrjPj4eGPMmDHG7373O7/jra2txrJly4zU1FQjPj7emDp1qnHo0KEwjRbd4Xa7jZ/85CfGqFGjjMGDBxtXXXWV8X/+z/8xmpubfedwXSPbnj17Ov13NDc31zCM7l2/zz77zJg3b54xbNgwIzEx0bjvvvsMj8cTsjHHGEa7pUQAAACAKEcGFwAAAKZCgQsAAABTocAFAACAqVDgAgAAwFQocAEAAGAqFLgAAAAwFQpcAAAAmAoFLgAAAEyFAhcAAACmQoELAAAAU6HABQAAgKn8P0gytbDhsbgDAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"SK8p_A2NST2l"},"source":["**Now plot (x,y) on both axes. And call your figure object to show it.**"]},{"cell_type":"markdown","metadata":{"id":"hYaQZfbFST2n"},"source":["## Exercise 3\n","\n","**Create the plot below by adding two axes to a figure object at [0,0,1,1] and [0.2,0.5,.4,.4]**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BuiYx0xbST2o","outputId":"b80434aa-3c4b-43ce-9555-c1f0896cc99f","colab":{"base_uri":"https://localhost:8080/","height":546},"executionInfo":{"status":"ok","timestamp":1765028460724,"user_tz":-330,"elapsed":24,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["fig=plt.figure()\n","ax1=fig.add_axes([0,0,1,1])\n","ax2=fig.add_axes([0.2,0.5,.4,.4])\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"SeRSiZDGST2o"},"source":["**Now use x,y, and z arrays to recreate the plot below. Notice the xlimits and y limits on the inserted plot:**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jhy7t5AKST2p","outputId":"1d4a48b5-f104-4924-b50d-13a7ae61e47f","colab":{"base_uri":"https://localhost:8080/","height":540},"executionInfo":{"status":"ok","timestamp":1765029982026,"user_tz":-330,"elapsed":266,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["fig=plt.figure()\n","ax1=fig.add_axes([0,0,1,1])\n","ax1.plot(x,z)\n","ax2=fig.add_axes([0.2,0.5,.4,.4])\n","ax2.plot(x,y)\n","ax2.set_xlim([20, 22])\n","ax2.set_ylim([30, 50])\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"TM6XOILJST2q"},"source":["## Exercise 4\n","\n","**Use plt.subplots(nrows=1, ncols=2) to create the plot below.**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"osk0Jco2ST2r","outputId":"7e787f75-ff76-4dbe-eed6-4c95e597fb9a","colab":{"base_uri":"https://localhost:8080/","height":430},"executionInfo":{"status":"ok","timestamp":1765030275685,"user_tz":-330,"elapsed":88,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["plt.subplot(1,2,1)\n","plt.plot()\n","plt.subplot(1,2,2)\n","plt.plot()\n","plt.show()\n"]},{"cell_type":"markdown","metadata":{"id":"QWwItNZVST2r"},"source":["**Now plot (x,y) and (x,z) on the axes. Play around with the linewidth and style**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZoEMYyLOST2r","outputId":"d747f555-936f-4919-8cbd-44b2ae58a7a4","colab":{"base_uri":"https://localhost:8080/","height":430},"executionInfo":{"status":"ok","timestamp":1765025143160,"user_tz":-330,"elapsed":226,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["plt.subplot(1,2,1)\n","plt.plot(x,y,linestyle='dashed')\n","plt.subplot(1,2,2)\n","plt.plot(x,z,color='r')\n","\n","\n","\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"hV08IzIvST2s"},"source":["**See if you can resize the plot by adding the figsize() argument in plt.subplots() are copying and pasting your previous code.**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0pq5vAFZST2s","outputId":"33425a62-1f99-44c7-9977-63308364a8a3","colab":{"base_uri":"https://localhost:8080/","height":178},"executionInfo":{"status":"ok","timestamp":1765025506681,"user_tz":-330,"elapsed":134,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x150 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["plt.figure(figsize=(10, 1.5))\n","plt.subplot(1,2,1)\n","plt.plot(x,y,linewidth=3)\n","plt.subplot(1,2,2)\n","plt.plot(x,z,color='r',linestyle=\"dashed\")\n","\n","\n","\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"-ONbbrWEST2t"},"source":["# Great Job!"]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.12"},"colab":{"provenance":[{"file_id":"1hYhHtDxal3ubd7qG8XFZDk8-XHoB_b1P","timestamp":1764870186292},{"file_id":"1TktwCMc95E3iZHIyOHJO0-h_UzQpVe4U","timestamp":1764745279737},{"file_id":"1tZurHlHqMzny5-rSxWhXrkRUpMckdLl5","timestamp":1731497437912}]}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/Assignment 1/Assignment1_SakshamAgarwal/SakshamAgarwal_Assignment1_Pandas.ipynb b/Assignment 1/Assignment1_SakshamAgarwal/SakshamAgarwal_Assignment1_Pandas.ipynb
new file mode 100644
index 00000000..82403dc9
--- /dev/null
+++ b/Assignment 1/Assignment1_SakshamAgarwal/SakshamAgarwal_Assignment1_Pandas.ipynb	
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"markdown","source":["## General Instructions for all Assignments"],"metadata":{"id":"AWDeauE3Bdzs"}},{"cell_type":"markdown","source":["1. Fork the repository and make a copy of this notebook and rename it as your name_Assignment1_Pandas\n","2. Generate a pull request. The pull request message should also be yourname_Assignment1"],"metadata":{"id":"Xaf9uMvXBJEC"}},{"cell_type":"markdown","source":["\n","1. This code notebook is composed of cells. Each cell is either text or Python code.\n","2. To run a cell of code, click the \"play button\" icon to the left of the cell or click on the cell and press \"Shift+Enter\" on your keyboard. This will execute the Python code contained in the cell. Executing a cell that defines a variable is important before executing or authoring a cell that depends on that previously created variable assignment.\n"],"metadata":{"id":"4_6uxHVGB_XT"}},{"cell_type":"markdown","source":[],"metadata":{"id":"b68xNjkKCBIF"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"BiD4gl6_cAYs"},"outputs":[],"source":["#import pandas library\n","import pandas as pd"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JUtm9KricAYu"},"outputs":[],"source":["df = pd.read_csv('/content/Iris.csv') #enter path for the Iris csv in the brackets\n","#reads the csv file and stores it in a data frame(define in the pandas library)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qzUnSkfRcAYu"},"outputs":[],"source":["dictionary = {'col':[4,2,3], 'col2':[4,7,6], 'col3':[7,10,9]}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ACswu4BXcAYu"},"outputs":[],"source":["dictionarydf = pd.DataFrame.from_dict(dictionary)\n","#it creats a new datafram which stores the dictionary made above"]},{"cell_type":"code","source":["df.head()\n","#shows top 5 rows (row indexing starts from 0)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"MvYNz8E2dp3K","executionInfo":{"status":"ok","timestamp":1765037141939,"user_tz":-330,"elapsed":82,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"b8d4ce12-cb8c-4227-99ee-42c22bf1a34d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n","0   1            5.1           3.5            1.4           0.2  Iris-setosa\n","1   2            4.9           3.0            1.4           0.2  Iris-setosa\n","2   3            4.7           3.2            1.3           0.2  Iris-setosa\n","3   4            4.6           3.1            1.5           0.2  Iris-setosa\n","4   5            5.0           3.6            1.4           0.2  Iris-setosa"],"text/html":["\n","  <div id=\"df-ed32e00f-2a2e-42c0-b0b0-9bb0f7972657\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Id</th>\n","      <th>SepalLengthCm</th>\n","      <th>SepalWidthCm</th>\n","      <th>PetalLengthCm</th>\n","      <th>PetalWidthCm</th>\n","      <th>Species</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>5.1</td>\n","      <td>3.5</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","   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       3.5            1.4           0.2  Iris-setosa\n","1   2            4.9           3.0            1.4           0.2  Iris-setosa\n","2   3            4.7           3.2            1.3           0.2  Iris-setosa\n","3   4            4.6           3.1            1.5           0.2  Iris-setosa\n","4   5            5.0           3.6            1.4           0.2  Iris-setosa\n","5   6            5.4           3.9            1.7           0.4  Iris-setosa\n","6   7            4.6           3.4            1.4           0.3  Iris-setosa\n","7   8            5.0           3.4            1.5           0.2  Iris-setosa\n","8   9            4.4           2.9            1.4           0.2  Iris-setosa\n","9  10            4.9           3.1            1.5           0.1  Iris-setosa"],"text/html":["\n","  <div id=\"df-3c27b44a-2a44-4e8f-9232-afe11a8b8b28\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: 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Agarwal","userId":"14846103380388986571"}},"outputId":"f11a7e5a-a3ab-4459-9819-aed5709cac1b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n","135  136            7.7           3.0            6.1           2.3   \n","136  137            6.3           3.4            5.6           2.4   \n","137  138            6.4           3.1            5.5           1.8   \n","138  139            6.0           3.0            4.8           1.8   \n","139  140            6.9           3.1            5.4           2.1   \n","140  141            6.7           3.1            5.6           2.4   \n","141  142            6.9           3.1            5.1           2.3   \n","142  143            5.8           2.7            5.1           1.9   \n","143  144            6.8           3.2            5.9           2.3   \n","144  145            6.7           3.3            5.7           2.5   \n","145  146            6.7           3.0            5.2           2.3   \n","146  147            6.3           2.5            5.0           1.9   \n","147  148            6.5           3.0            5.2           2.0   \n","148  149            6.2           3.4            5.4           2.3   \n","149  150            5.9           3.0            5.1           1.8   \n","\n","            Species  \n","135  Iris-virginica  \n","136  Iris-virginica  \n","137  Iris-virginica  \n","138  Iris-virginica  \n","139  Iris-virginica  \n","140  Iris-virginica  \n","141  Iris-virginica  \n","142  Iris-virginica  \n","143  Iris-virginica  \n","144  Iris-virginica  \n","145  Iris-virginica  \n","146  Iris-virginica  \n","147  Iris-virginica  \n","148  Iris-virginica  \n","149  Iris-virginica  "],"text/html":["\n","  <div id=\"df-4815a4ae-154f-430a-a28b-ae4766f8e111\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n"," 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SepalLengthCm, SepalWidthCm, PetalLengthCm, PetalWidthCm, Species]\n","Index: []"],"text/html":["\n","  <div id=\"df-83ee118e-8f6d-4de0-ac17-a33f5424a9aa\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Id</th>\n","      <th>SepalLengthCm</th>\n","      <th>SepalWidthCm</th>\n","      <th>PetalLengthCm</th>\n","      <th>PetalWidthCm</th>\n","      <th>Species</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df","summary":"{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43,\n        \"min\": 1,\n        \"max\": 150,\n        \"num_unique_values\": 150,\n        \"samples\": [\n          74,\n         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\n","dtypes: float64(4), int64(1), object(1)\n","memory usage: 7.2+ KB\n","None\n"]}],"source":["print(df.info())#show the data types of each feature or column name"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lT3OEUNDcAYv","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765037204226,"user_tz":-330,"elapsed":25,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"cc50c7aa-e215-4fbb-c209-85c549090240"},"outputs":[{"output_type":"stream","name":"stdout","text":["               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n","count  150.000000     150.000000    150.000000     150.000000    150.000000\n","mean    75.500000       5.843333      3.054000       3.758667      1.198667\n","std     43.445368       0.828066      0.433594       1.764420      0.763161\n","min      1.000000       4.300000      2.000000       1.000000      0.100000\n","25%     38.250000       5.100000      2.800000       1.600000      0.300000\n","50%     75.500000       5.800000      3.000000       4.350000      1.300000\n","75%    112.750000       6.400000      3.300000       5.100000      1.800000\n","max    150.000000       7.900000      4.400000       6.900000      2.500000\n"]}],"source":["print(df.describe())#describe the data through statistics"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"Z_52ct0BcAYw","executionInfo":{"status":"ok","timestamp":1765037451036,"user_tz":-330,"elapsed":57,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"42b686b5-efcd-4998-cef1-5f3a94e0bc03"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["     SepalLengthCm  SepalWidthCm\n","0              5.1           3.5\n","1              4.9           3.0\n","2              4.7           3.2\n","3              4.6           3.1\n","4              5.0           3.6\n","..             ...           ...\n","145            6.7           3.0\n","146            6.3           2.5\n","147            6.5           3.0\n","148            6.2           3.4\n","149            5.9           3.0\n","\n","[150 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-e7adb543-1ede-44b6-a1fc-2085d673807b\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>SepalLengthCm</th>\n","      <th>SepalWidthCm</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>5.1</td>\n","      <td>3.5</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4.9</td>\n","      <td>3.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>4.7</td>\n","      <td>3.2</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.6</td>\n","      <td>3.1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.0</td>\n","      <td>3.6</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>145</th>\n","      <td>6.7</td>\n","      <td>3.0</td>\n","    </tr>\n","    <tr>\n","      <th>146</th>\n","      <td>6.3</td>\n","      <td>2.5</td>\n","    </tr>\n","    <tr>\n","      <th>147</th>\n","      <td>6.5</td>\n","      <td>3.0</td>\n","    </tr>\n","    <tr>\n","      <th>148</th>\n","      <td>6.2</td>\n","      <td>3.4</td>\n","    </tr>\n","    <tr>\n","      <th>149</th>\n","      <td>5.9</td>\n","      <td>3.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>150 rows × 2 columns</p>\n","</div>\n","    <div 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\"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":46}],"source":["df[['SepalLengthCm', 'SepalWidthCm']]\n","#From taking multiple columns from the dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JWYXoLl0cAYw","executionInfo":{"status":"ok","timestamp":1765037454607,"user_tz":-330,"elapsed":12,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"c2669509-dfd0-423b-e40a-951cf3141022"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([5.1, 4.9, 4.7, 4.6, 5. , 5.4, 4.4, 4.8, 4.3, 5.8, 5.7, 5.2, 5.5,\n","       4.5, 5.3, 7. , 6.4, 6.9, 6.5, 6.3, 6.6, 5.9, 6. , 6.1, 5.6, 6.7,\n","       6.2, 6.8, 7.1, 7.6, 7.3, 7.2, 7.7, 7.4, 7.9])"]},"metadata":{},"execution_count":47}],"source":["df.SepalLengthCm.unique()\n","#gives the unique values of Sepal Length present in that particular column"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MiOVp1WacAYw","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765036165446,"user_tz":-330,"elapsed":15,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"7520ab9e-2168-4e9c-b2da-87a4961149e9"},"outputs":[{"output_type":"stream","name":"stdout","text":["Id                        11\n","SepalLengthCm            5.4\n","SepalWidthCm             3.7\n","PetalLengthCm            1.5\n","PetalWidthCm             0.2\n","Species          Iris-setosa\n","Name: 10, dtype: object\n"]}],"source":["print(df.loc[10])#Get the 11th row from the dataset\n","#Hint: for taking 11th row do we use 11 or 10 index?"]},{"cell_type":"code","source":["df.iloc[10,-1]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"TF03jh_MovET","executionInfo":{"status":"ok","timestamp":1765037245162,"user_tz":-330,"elapsed":23,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"472e4e70-9481-45ad-f3b6-c46ef9ef05ca"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Iris-setosa'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":37}]},{"cell_type":"code","source":["df.iloc[9,4]#Display the second last entry of the 10th row"],"metadata":{"id":"gV-h3rK-g5lZ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765037342221,"user_tz":-330,"elapsed":21,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"61782109-27fc-4516-e5dc-20049b6a821e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["np.float64(0.1)"]},"metadata":{},"execution_count":40}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"KXWCH34ecAYx","executionInfo":{"status":"ok","timestamp":1765037346294,"user_tz":-330,"elapsed":37,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"77986851-842c-45a3-ec84-beb085fc152f"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["    Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n","22  23            4.6           3.6            1.0           0.2  Iris-setosa\n","23  24            5.1           3.3            1.7           0.5  Iris-setosa\n","24  25            4.8           3.4            1.9           0.2  Iris-setosa\n","25  26            5.0           3.0            1.6           0.2  Iris-setosa\n","26  27            5.0           3.4            1.6           0.4  Iris-setosa\n","27  28            5.2           3.5            1.5           0.2  Iris-setosa\n","28  29            5.2           3.4            1.4           0.2  Iris-setosa\n","29  30            4.7           3.2            1.6           0.2  Iris-setosa\n","30  31            4.8           3.1            1.6           0.2  Iris-setosa\n","31  32            5.4           3.4            1.5           0.4  Iris-setosa\n","32  33            5.2           4.1            1.5           0.1  Iris-setosa"],"text/html":["\n","  <div id=\"df-e3d9afdd-aad4-4a2e-b99b-b5155928e648\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: 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<td>4.8</td>\n","      <td>3.1</td>\n","      <td>1.6</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>32</td>\n","      <td>5.4</td>\n","      <td>3.4</td>\n","      <td>1.5</td>\n","      <td>0.4</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>32</th>\n","      <td>33</td>\n","      <td>5.2</td>\n","      <td>4.1</td>\n","      <td>1.5</td>\n","      <td>0.1</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e3d9afdd-aad4-4a2e-b99b-b5155928e648')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path 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       \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28982753492378865,\n        \"min\": 3.0,\n        \"max\": 4.1,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.3,\n          3.2,\n          3.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22033033051637385,\n        \"min\": 1.0,\n        \"max\": 1.9,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1.0,\n          1.7,\n          1.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12135597524338358,\n        \"min\": 0.1,\n        \"max\": 0.5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.5,\n          0.1,\n          0.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Iris-setosa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":41}],"source":["df.iloc[22:33]\n","#takes everything from row 23(included) and 34(not included)"]},{"cell_type":"code","source":["df1=df.copy()\n","df1.dropna(axis=0, inplace=False) #drops any values that are null or na\n","print(df1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"quhmLFx-q6vS","executionInfo":{"status":"ok","timestamp":1765037417638,"user_tz":-330,"elapsed":26,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"fe8d2cf2-c6f3-459d-956b-c1da445ebf6d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n","0      1            5.1           3.5            1.4           0.2   \n","1      2            4.9           3.0            1.4           0.2   \n","2      3            4.7           3.2            1.3           0.2   \n","3      4            4.6           3.1            1.5           0.2   \n","4      5            5.0           3.6            1.4           0.2   \n","..   ...            ...           ...            ...           ...   \n","145  146            6.7           3.0            5.2           2.3   \n","146  147            6.3           2.5            5.0           1.9   \n","147  148            6.5           3.0            5.2           2.0   \n","148  149            6.2           3.4            5.4           2.3   \n","149  150            5.9           3.0            5.1           1.8   \n","\n","            Species  \n","0       Iris-setosa  \n","1       Iris-setosa  \n","2       Iris-setosa  \n","3       Iris-setosa  \n","4       Iris-setosa  \n","..              ...  \n","145  Iris-virginica  \n","146  Iris-virginica  \n","147  Iris-virginica  \n","148  Iris-virginica  \n","149  Iris-virginica  \n","\n","[150 rows x 6 columns]\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kZg8z57fcAY2"},"outputs":[],"source":["df.dropna(axis=0, inplace=True)\n","#drops all the rows with missing values"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":272},"id":"FRWcDJA2cAY2","executionInfo":{"status":"ok","timestamp":1765037430455,"user_tz":-330,"elapsed":30,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"4fc05876-9af1-4e39-e97c-554d0591e932"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Id               0\n","SepalLengthCm    0\n","SepalWidthCm     0\n","PetalLengthCm    0\n","PetalWidthCm     0\n","Species          0\n","dtype: int64"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Id</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>SepalLengthCm</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>SepalWidthCm</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>PetalLengthCm</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>PetalWidthCm</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>Species</th>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> int64</label>"]},"metadata":{},"execution_count":45}],"source":["df.isnull().sum()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JioW0K_pcAY3"},"outputs":[],"source":["df.to_csv('cleandata_iris.csv')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"asToKeGJcAY4"},"outputs":[],"source":["del df"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.4"},"colab":{"provenance":[{"file_id":"1tVNRnxnutLstrGKY3P0fbmpEVSwXiDnJ","timestamp":1765033556008},{"file_id":"1PJDPRd4uOSxvoSK0o_O22PiQ2-AkDis4","timestamp":1764744257366},{"file_id":"1AjoKNd7ieBCZ4NDPgzWDLDMsHKuYsb5A","timestamp":1731497483327}]}},"nbformat":4,"nbformat_minor":0}
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+{"cells":[{"cell_type":"markdown","metadata":{"id":"HfJB9JnS9xlK"},"source":["# NumPy Exercises\n","\n","Now that we've learned about NumPy let's test your knowledge. We'll start off with a few simple tasks, and then you'll be asked some more complicated questions."]},{"cell_type":"markdown","metadata":{"id":"u9POEVg29xlM"},"source":["#### Import NumPy as np"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yyS-PuO_9xlM"},"outputs":[],"source":["import numpy as np"]},{"cell_type":"markdown","metadata":{"id":"ehlpKUPc9xlM"},"source":["#### Create an array of 10 zeros"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aEQkK-Dw9xlN","outputId":"1c131f2c-af63-43ef-aebb-474b4e27a4b2"},"outputs":[{"data":{"text/plain":["array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["arr = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0])"]},{"cell_type":"markdown","metadata":{"id":"6QHp05Ei9xlN"},"source":["#### Create an array of 10 ones"]},{"cell_type":"code","execution_count":null,"metadata":{"scrolled":true,"id":"ebe9xrC29xlN","outputId":"cbbacbb1-6ba4-4a97-a0fa-a6f449b13779","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764783524704,"user_tz":-330,"elapsed":29,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[1 1 1 1 1 1 1 1 1 1]\n"]}],"source":["arr=np.full(10,1)\n","print(arr)"]},{"cell_type":"markdown","metadata":{"id":"ziS-l3xp9xlO"},"source":["#### Create an array of 10 fives"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fFC7bqLM9xlO","outputId":"227a3fd5-c440-450d-9d5f-59494ab3ff85","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764783543104,"user_tz":-330,"elapsed":29,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[1 1 1 1 1 1 1 1 1 1]\n"]}],"source":["arr=np.full(10,1)\n","print(arr)"]},{"cell_type":"markdown","metadata":{"id":"kBugMXlC9xlO"},"source":["#### Create an array of the integers from 10 to 50"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JsO6qS9R9xlO","outputId":"a5c3f05f-db7d-4c37-a15c-c230529bacac","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764783558543,"user_tz":-330,"elapsed":14,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33\n"," 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50]\n"]}],"source":["arr=np.arange(10,51)\n","print(arr)"]},{"cell_type":"markdown","metadata":{"id":"dMw4V2L79xlO"},"source":["#### Create an array of all the even integers from 10 to 50"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4lK-5SQV9xlO","outputId":"70d187ec-b499-48f2-aaa0-5e8fc9aadd20","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764783582047,"user_tz":-330,"elapsed":26,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50]\n"]}],"source":["arr=np.arange(10,51,2)\n","print(arr)"]},{"cell_type":"markdown","metadata":{"id":"g__F0rB39xlP"},"source":["#### Create a 3x3 matrix with values ranging from 0 to 8"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MmVXCn0K9xlP","outputId":"25a821bc-220f-4afd-b0b2-6c77c0dfc812","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764783690729,"user_tz":-330,"elapsed":28,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[[0 1 2]\n"," [3 4 5]\n"," [6 7 8]]\n"]}],"source":["arr=np.arange(0,9)\n","arr=arr.reshape(3,3)\n","print(arr)"]},{"cell_type":"markdown","metadata":{"id":"4YfT0aLo9xlP"},"source":["#### Create a 3x3 identity matrix"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UHa42UpQ9xlP","outputId":"2f5b6a04-7d06-4770-cdcb-50e44762c8c3","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764783881093,"user_tz":-330,"elapsed":38,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[[1. 0. 0.]\n"," [0. 1. 0.]\n"," [0. 0. 1.]]\n"]}],"source":["arr=np.identity(3)\n","print(arr)"]},{"cell_type":"markdown","metadata":{"id":"bfwDbjhI9xlP"},"source":["#### Use NumPy to generate a random number between 0 and 1"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Z0OroZxW9xlP","outputId":"9fb66c74-7963-44b1-853a-08afc9d479d5","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765037674591,"user_tz":-330,"elapsed":12,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["0.7210668988630553\n"]}],"source":["print(np.random.rand())"]},{"cell_type":"markdown","metadata":{"id":"rLW0Jjzp9xlP"},"source":["#### Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"szluy14n9xlP","outputId":"1190ff62-7984-4d18-ee8b-612b03378c8a","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765037956035,"user_tz":-330,"elapsed":15,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[-0.76238469  0.83347474  0.33969207 -1.16913462 -1.00629952 -0.02417897\n","  0.4224093   0.86707434  1.10954394  0.32828816  0.47720936  1.14830247\n"," -0.11463323 -0.23041961  1.01458838  1.58144574 -0.08504101 -1.0065272\n"," -1.19019504 -0.57174949  0.84928131  0.51261494  0.06792625 -0.78699475\n"," -0.60657134]\n"]}],"source":["arr=np.random.randn(25)\n","print(arr)"]},{"cell_type":"markdown","metadata":{"id":"_GhI8LYn9xlP"},"source":["#### Create the following matrix:"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wS1ZBddV9xlP","outputId":"a0bd6e57-ee7b-45e5-9957-d64d20f2aa3b","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764785441779,"user_tz":-330,"elapsed":15,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[[0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 ]\n"," [0.11 0.12 0.13 0.14 0.15 0.16 0.17 0.18 0.19 0.2 ]\n"," [0.21 0.22 0.23 0.24 0.25 0.26 0.27 0.28 0.29 0.3 ]\n"," [0.31 0.32 0.33 0.34 0.35 0.36 0.37 0.38 0.39 0.4 ]\n"," [0.41 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.5 ]\n"," [0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59 0.6 ]\n"," [0.61 0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.7 ]\n"," [0.71 0.72 0.73 0.74 0.75 0.76 0.77 0.78 0.79 0.8 ]\n"," [0.81 0.82 0.83 0.84 0.85 0.86 0.87 0.88 0.89 0.9 ]\n"," [0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99 1.  ]]\n"]}],"source":["arr=np.arange(0.01,1.01,0.01)\n","arr=arr.reshape(10,10)\n","print(arr)"]},{"cell_type":"markdown","metadata":{"id":"3OqA-QtL9xlQ"},"source":["#### Create an array of 20 linearly spaced points between 0 and 1:"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FNXTugQ29xlQ","outputId":"77c82e39-3d11-4688-8019-e9467991a49a","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765038276655,"user_tz":-330,"elapsed":11,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[[0.         0.05263158 0.10526316 0.15789474 0.21052632]\n"," [0.26315789 0.31578947 0.36842105 0.42105263 0.47368421]\n"," [0.52631579 0.57894737 0.63157895 0.68421053 0.73684211]\n"," [0.78947368 0.84210526 0.89473684 0.94736842 1.        ]]\n"]}],"source":["arr=np.linspace(0,1,20)\n","arr=arr.reshape(4,5)\n","print(arr)"]},{"cell_type":"markdown","metadata":{"id":"elx0EaxE9xlQ"},"source":["## Numpy Indexing and Selection\n","\n","Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs:"]},{"cell_type":"markdown","metadata":{"id":"2Tbm9kVf9xlQ"},"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ft8P8e249xlQ","outputId":"70f345a8-88a2-47d6-f23b-669fb606f0a0","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764786048427,"user_tz":-330,"elapsed":27,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[[ 1  2  3  4  5]\n"," [ 6  7  8  9 10]\n"," [11 12 13 14 15]\n"," [16 17 18 19 20]\n"," [21 22 23 24 25]]\n"]}],"source":["arr=np.arange(1,26)\n","arr=arr.reshape(5,5)\n","print(arr)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WrcFkpGL9xlQ","outputId":"01d29308-8f64-47a3-ccf2-f827294398a4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764786847562,"user_tz":-330,"elapsed":476,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[[12 13 14 15]\n"," [17 18 19 20]\n"," [22 23 24 25]]\n"]}],"source":["column_1=np.arange(12,23,5).reshape(-1,1)\n","column_2=np.arange(13,24,5).reshape(-1,1)\n","column_3=np.arange(14,25,5).reshape(-1,1)\n","column_4=np.arange(15,26,5).reshape(-1,1)\n","arr=np.hstack([column_1,column_2,column_3,column_4])\n","print(arr)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rnUSntEa9xlQ","outputId":"d5b32dec-36c9-41a1-c6de-aaefc84ff158"},"outputs":[{"data":{"text/plain":["20"]},"execution_count":41,"metadata":{},"output_type":"execute_result"}],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3DMBC_wp9xlQ","outputId":"88ac5531-4252-42cc-8fba-58d8946fa07a","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764786002493,"user_tz":-330,"elapsed":25,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[[ 2]\n"," [ 7]\n"," [12]]\n"]}],"source":["arr=np.arange(2,13,5)\n","arr=arr.reshape(3,1)\n","print(arr)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pGjD4HUK9xlQ","outputId":"effd7b9d-3e8e-4a0f-b892-24826c622ad6"},"outputs":[{"data":{"text/plain":["array([21, 22, 23, 24, 25])"]},"execution_count":54,"metadata":{},"output_type":"execute_result"}],"source":["arr=np.arange(16,26)\n","\n","print(arr)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dqsdpxUo9xlR","outputId":"eb61827f-c22d-4143-f92f-381f5688d8c4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764785860686,"user_tz":-330,"elapsed":13,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[[16 17 18 19 20]\n"," [21 22 23 24 25]]\n"]}],"source":["arr=np.arange(16,26)\n","arr=arr.reshape(2,5)\n","print(arr)"]},{"cell_type":"markdown","metadata":{"collapsed":true,"id":"6vtHuVJU9xlf"},"source":["# Great Job!"]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.12"},"colab":{"provenance":[{"file_id":"1uvwMRRqMFNlaYoWGT6JhFghskgPy9dzJ","timestamp":1764783482056},{"file_id":"1PacBskAxI7FPP4LIyB_3MysbL8ArUHsV","timestamp":1764745354238},{"file_id":"19ZajGBzqADvFnQ0kzbkad_udXeAh26pj","timestamp":1731497620277}]}},"nbformat":4,"nbformat_minor":0}
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diff --git a/Assignment 3/Assignment3_SakshamAgarwal/Assignment3_SakshamAgarwal.ipynb b/Assignment 3/Assignment3_SakshamAgarwal/Assignment3_SakshamAgarwal.ipynb
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+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyPhJHga/gnVxUvZMWnxc+lP"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":6,"metadata":{"id":"nn6n7OMV2HVz","executionInfo":{"status":"ok","timestamp":1767528879888,"user_tz":-330,"elapsed":5,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"outputs":[],"source":["import tensorflow as tf\n","from tensorflow.keras import layers, models\n","from tensorflow.keras.regularizers import l2\n","import numpy as np\n","import matplotlib.pyplot as plt"]},{"cell_type":"code","source":["img_size = (128, 128)\n","batch_size = 32"],"metadata":{"id":"773y2JB52ffz","executionInfo":{"status":"ok","timestamp":1767528879955,"user_tz":-330,"elapsed":65,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["from zipfile import ZipFile\n","with ZipFile('/content/archive.zip', 'r') as zip_ref:\n","    zip_ref.extractall('/content/images')\n","print(\"Extraction done!\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Dxv0rd7l3Eqp","executionInfo":{"status":"ok","timestamp":1767528894495,"user_tz":-330,"elapsed":438,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"7da354e9-28cb-4368-a421-f301dd18f00f"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["Extraction done!\n"]}]},{"cell_type":"code","source":["import os\n","for root, dirs, files in os.walk('/content/images'):\n","    print(root)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VsGvKw5l3FAR","executionInfo":{"status":"ok","timestamp":1767528897695,"user_tz":-330,"elapsed":19,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"e430593b-c639-49d0-a9c9-ee3d5ea2443a"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/images\n","/content/images/Train\n","/content/images/Train/Up\n","/content/images/Train/Down\n","/content/images/Test\n","/content/images/Test/Up\n","/content/images/Test/Down\n"]}]},{"cell_type":"code","source":["train_ds = tf.keras.utils.image_dataset_from_directory(\"/content/images/Train\",image_size=(128,128),batch_size=32,\n","label_mode=\"binary\",shuffle=True,seed=42)\n","\n","test_ds = tf.keras.utils.image_dataset_from_directory(\"/content/images/Test\",image_size=(128,128),batch_size=32,\n","label_mode=\"binary\",shuffle=False)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gI2L4Rr-2iUi","executionInfo":{"status":"ok","timestamp":1767528903524,"user_tz":-330,"elapsed":3261,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"b9304ccb-a40b-43c1-db77-fbddc7d1b94c"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["Found 1433 files belonging to 2 classes.\n","Found 351 files belonging to 2 classes.\n"]}]},{"cell_type":"code","source":["data_augmentation = tf.keras.Sequential([layers.RandomFlip(\"horizontal\"),layers.RandomRotation(0.1),layers.RandomZoom(0.1),\n","])\n","AUTOTUNE = tf.data.AUTOTUNE\n","train_ds = train_ds.map(lambda x, y: (data_augmentation(x) / 255.0, y),\n","    num_parallel_calls=AUTOTUNE\n",").prefetch(AUTOTUNE)\n","\n","test_ds = test_ds.map(lambda x, y: (x / 255.0, y),\n","    num_parallel_calls=AUTOTUNE\n",").prefetch(AUTOTUNE)\n"],"metadata":{"id":"y_0yLmvw3kec","executionInfo":{"status":"ok","timestamp":1767528911456,"user_tz":-330,"elapsed":213,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"execution_count":15,"outputs":[]},{"cell_type":"code","source":["model = models.Sequential([\n","    layers.Conv2D(16,(3,3), activation='relu',kernel_regularizer=l2(0.001),input_shape=(128,128,3)),\n","    layers.MaxPooling2D(),\n","    layers.Conv2D(32,(3,3),activation='relu',kernel_regularizer=l2(0.001)),\n","    layers.MaxPooling2D(),\n","    layers.Conv2D(64,(3,3), activation='relu',kernel_regularizer=l2(0.001)),\n","    layers.MaxPooling2D(),\n","    layers.Flatten(),\n","    layers.Dense(128, activation='relu'),\n","    layers.Dropout(0.6),\n","    layers.Dense(1, activation='sigmoid')\n","])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xVl2okx43sxJ","executionInfo":{"status":"ok","timestamp":1767528914378,"user_tz":-330,"elapsed":898,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"cae83056-0cfb-441c-9149-be48a6a4298f"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n","  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]}]},{"cell_type":"code","source":["model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),loss=\"binary_crossentropy\",metrics=[\"accuracy\"])"],"metadata":{"id":"urGPJ6dU3zsk","executionInfo":{"status":"ok","timestamp":1767528918948,"user_tz":-330,"elapsed":6,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}}},"execution_count":17,"outputs":[]},{"cell_type":"code","source":["model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":469},"id":"jSEWa7P932Ia","executionInfo":{"status":"ok","timestamp":1767528920673,"user_tz":-330,"elapsed":50,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"ac3d9a49-459e-4705-bfac-f3db7438fa15"},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_1\"\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m126\u001b[0m, \u001b[38;5;34m126\u001b[0m, \u001b[38;5;34m16\u001b[0m)   │           \u001b[38;5;34m448\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m63\u001b[0m, \u001b[38;5;34m63\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m61\u001b[0m, \u001b[38;5;34m61\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │         \u001b[38;5;34m4,640\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12544\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │     \u001b[38;5;34m1,605,760\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m129\u001b[0m │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">126</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">126</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">448</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">63</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">63</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">61</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">61</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,640</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12544</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,605,760</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,629,473\u001b[0m (6.22 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,629,473</span> (6.22 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,629,473\u001b[0m (6.22 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,629,473</span> (6.22 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n","</pre>\n"]},"metadata":{}}]},{"cell_type":"code","source":["history = model.fit(train_ds,epochs=15,validation_data=test_ds)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Y8MVruFg34fm","executionInfo":{"status":"ok","timestamp":1767529064007,"user_tz":-330,"elapsed":140714,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"9e1134c0-9878-4708-e4f9-cd5a2903424d"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/15\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 260ms/step - accuracy: 0.5333 - loss: 0.7631 - val_accuracy: 0.5527 - val_loss: 0.7633\n","Epoch 2/15\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 165ms/step - accuracy: 0.5527 - loss: 0.7610 - val_accuracy: 0.5527 - val_loss: 0.7540\n","Epoch 3/15\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 190ms/step - accuracy: 0.5591 - loss: 0.7532 - val_accuracy: 0.5499 - val_loss: 0.7544\n","Epoch 4/15\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 216ms/step - accuracy: 0.5652 - loss: 0.7535 - val_accuracy: 0.5499 - val_loss: 0.7511\n","Epoch 5/15\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 164ms/step - accuracy: 0.5589 - loss: 0.7466 - val_accuracy: 0.5499 - val_loss: 0.7487\n","Epoch 6/15\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 186ms/step - accuracy: 0.5708 - loss: 0.7431 - val_accuracy: 0.5499 - val_loss: 0.7488\n","Epoch 7/15\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 187ms/step - accuracy: 0.5531 - loss: 0.7475 - val_accuracy: 0.5527 - val_loss: 0.7480\n","Epoch 8/15\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 173ms/step - accuracy: 0.5607 - loss: 0.7433 - val_accuracy: 0.5499 - val_loss: 0.7439\n","Epoch 9/15\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 193ms/step - accuracy: 0.5517 - loss: 0.7445 - val_accuracy: 0.5499 - val_loss: 0.7418\n","Epoch 10/15\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 198ms/step - accuracy: 0.5545 - loss: 0.7401 - val_accuracy: 0.5499 - val_loss: 0.7401\n","Epoch 11/15\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 171ms/step - accuracy: 0.5708 - loss: 0.7379 - val_accuracy: 0.5556 - val_loss: 0.7367\n","Epoch 12/15\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 187ms/step - accuracy: 0.5682 - loss: 0.7387 - val_accuracy: 0.5470 - val_loss: 0.7361\n","Epoch 13/15\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 187ms/step - accuracy: 0.5581 - loss: 0.7362 - val_accuracy: 0.5527 - val_loss: 0.7354\n","Epoch 14/15\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 168ms/step - accuracy: 0.5662 - loss: 0.7317 - val_accuracy: 0.5499 - val_loss: 0.7344\n","Epoch 15/15\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 187ms/step - accuracy: 0.5437 - loss: 0.7350 - val_accuracy: 0.5470 - val_loss: 0.7342\n"]}]},{"cell_type":"code","source":["epochs=range(1,len(history.history['accuracy']) + 1)\n","plt.figure()\n","plt.plot(epochs,history.history['accuracy'])\n","plt.plot(epochs,history.history['val_accuracy'])\n","plt.xlabel('Epochs')\n","plt.ylabel('Accuracy')\n","plt.title('Training vs Validation Accuracy')\n","plt.legend(['Training Accuracy', 'Validation Accuracy'])\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"x__6iJJg56Z6","executionInfo":{"status":"ok","timestamp":1767529067058,"user_tz":-330,"elapsed":327,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"1a932c5e-8011-4aa1-e833-4431db3130c7"},"execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["plt.figure()\n","plt.plot(epochs, history.history['loss'])\n","plt.plot(epochs, history.history['val_loss'])\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss')\n","plt.title('Training vs Validation Loss')\n","plt.legend(['Training Loss', 'Validation Loss'])\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"m36ud0Cc6SA8","executionInfo":{"status":"ok","timestamp":1767529070463,"user_tz":-330,"elapsed":186,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"d1fbe68f-e11c-415f-cd54-424d61fb79e8"},"execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":[],"metadata":{"id":"zPX9WH4e3IVV"}}]}
\ No newline at end of file
diff --git a/MidEval Code/MidEval_SakshamAgarwal.ipynb b/MidEval Code/MidEval_SakshamAgarwal.ipynb
new file mode 100644
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+++ b/MidEval Code/MidEval_SakshamAgarwal.ipynb	
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyNOx96RzpPP5cMDUrZG16In"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"uFJExNyKjWAL"},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import StandardScaler, OneHotEncoder\n","from sklearn.compose import ColumnTransformer\n","from sklearn.pipeline import Pipeline\n","from sklearn.linear_model import LogisticRegression\n","import tensorflow as tf\n","from tensorflow import keras\n","from tensorflow.keras import layers\n","from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score,confusion_matrix"]},{"cell_type":"code","source":["df=pd.read_csv('/content/sample_data/quantvision_financial_dataset_200.csv') #loading data set\n"],"metadata":{"id":"TnojnbiuqmOM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df=df.dropna().reset_index(drop=True) #dropping the empty rows in dataset"],"metadata":{"id":"WWPk_3E5qpg4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#assigning input features to x and target to y\n","x=df[[\"lookback_days\",\"asset_type\",\"market_regime\",\"high_volatility\",\"technical_score\", \"edge_density\",\n","    \"slope_strength\", \"candlestick_variance\",\"pattern_symmetry\"]]\n","y=df[\"future_trend\"]"],"metadata":{"id":"ejUrtmxUxR6m"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#classifying numeric and string inputs\n","numeric_features=[\"lookback_days\",\"high_volatility\",\"technical_score\", \"edge_density\",\n","                  \"slope_strength\", \"candlestick_variance\",\"pattern_symmetry\"]\n","categorical_features=[\"asset_type\",\"market_regime\"]"],"metadata":{"id":"_4Q1HAizquJE"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#standardises all input features\n","numeric_transformer = StandardScaler()\n","categorical_transformer = OneHotEncoder(drop=\"first\")\n","\n","preprocess=ColumnTransformer(\n","    transformers=[\n","        (\"num\",numeric_transformer,numeric_features),\n","        (\"cat\",categorical_transformer,categorical_features)\n","    ]\n",")"],"metadata":{"id":"jwSX9uiwtoEF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#splitting respectively for training and testing purpose\n","x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=35)"],"metadata":{"id":"kHaBOZ-FjkpJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#implementing logistic regression by using pipeline\n","clf=Pipeline(steps=[(\"preprocessor\",preprocess),(\"classifier\",LogisticRegression(max_iter=800,random_state=35))])"],"metadata":{"id":"70jRNx7_tv5J"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#fitting the training samples in the pipeline\n","clf.fit(x_train,y_train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":245},"id":"ODPx_zZ1ivwy","executionInfo":{"status":"ok","timestamp":1766429868950,"user_tz":-330,"elapsed":50,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"75b91385-a082-4013-e135-7af1330fe6e5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Pipeline(steps=[('preprocessor',\n","                 ColumnTransformer(transformers=[('num', StandardScaler(),\n","                                                  ['lookback_days',\n","                                                   'high_volatility',\n","                                                   'technical_score',\n","                                                   'edge_density',\n","                                                   'slope_strength',\n","                                                   'candlestick_variance',\n","                                                   'pattern_symmetry']),\n","                                                 ('cat',\n","                                                  OneHotEncoder(drop='first'),\n","                                                  ['asset_type',\n","                                                   'market_regime'])])),\n","                ('classifier',\n","                 LogisticRegression(max_iter=800, random_state=35))])"],"text/html":["<style>#sk-container-id-4 {\n","  /* Definition of color scheme common for light and dark mode */\n","  --sklearn-color-text: #000;\n","  --sklearn-color-text-muted: #666;\n","  --sklearn-color-line: gray;\n","  /* Definition of color scheme for unfitted estimators */\n","  --sklearn-color-unfitted-level-0: #fff5e6;\n","  --sklearn-color-unfitted-level-1: #f6e4d2;\n","  --sklearn-color-unfitted-level-2: #ffe0b3;\n","  --sklearn-color-unfitted-level-3: chocolate;\n","  /* Definition of color scheme for fitted estimators */\n","  --sklearn-color-fitted-level-0: #f0f8ff;\n","  --sklearn-color-fitted-level-1: #d4ebff;\n","  --sklearn-color-fitted-level-2: #b3dbfd;\n","  --sklearn-color-fitted-level-3: cornflowerblue;\n","\n","  /* Specific color for light theme */\n","  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n","  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-icon: #696969;\n","\n","  @media (prefers-color-scheme: dark) {\n","    /* Redefinition of color scheme for dark theme */\n","    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n","    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-icon: #878787;\n","  }\n","}\n","\n","#sk-container-id-4 {\n","  color: var(--sklearn-color-text);\n","}\n","\n","#sk-container-id-4 pre {\n","  padding: 0;\n","}\n","\n","#sk-container-id-4 input.sk-hidden--visually {\n","  border: 0;\n","  clip: rect(1px 1px 1px 1px);\n","  clip: rect(1px, 1px, 1px, 1px);\n","  height: 1px;\n","  margin: -1px;\n","  overflow: hidden;\n","  padding: 0;\n","  position: absolute;\n","  width: 1px;\n","}\n","\n","#sk-container-id-4 div.sk-dashed-wrapped {\n","  border: 1px dashed var(--sklearn-color-line);\n","  margin: 0 0.4em 0.5em 0.4em;\n","  box-sizing: border-box;\n","  padding-bottom: 0.4em;\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","#sk-container-id-4 div.sk-container {\n","  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n","     but bootstrap.min.css set `[hidden] { display: none !important; }`\n","     so we also need the `!important` here to be able to override the\n","     default hidden behavior on the sphinx rendered scikit-learn.org.\n","     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n","  display: inline-block !important;\n","  position: relative;\n","}\n","\n","#sk-container-id-4 div.sk-text-repr-fallback {\n","  display: none;\n","}\n","\n","div.sk-parallel-item,\n","div.sk-serial,\n","div.sk-item {\n","  /* draw centered vertical line to link estimators */\n","  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n","  background-size: 2px 100%;\n","  background-repeat: no-repeat;\n","  background-position: center center;\n","}\n","\n","/* Parallel-specific style estimator block */\n","\n","#sk-container-id-4 div.sk-parallel-item::after {\n","  content: \"\";\n","  width: 100%;\n","  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n","  flex-grow: 1;\n","}\n","\n","#sk-container-id-4 div.sk-parallel {\n","  display: flex;\n","  align-items: stretch;\n","  justify-content: center;\n","  background-color: var(--sklearn-color-background);\n","  position: relative;\n","}\n","\n","#sk-container-id-4 div.sk-parallel-item {\n","  display: flex;\n","  flex-direction: column;\n","}\n","\n","#sk-container-id-4 div.sk-parallel-item:first-child::after {\n","  align-self: flex-end;\n","  width: 50%;\n","}\n","\n","#sk-container-id-4 div.sk-parallel-item:last-child::after {\n","  align-self: flex-start;\n","  width: 50%;\n","}\n","\n","#sk-container-id-4 div.sk-parallel-item:only-child::after {\n","  width: 0;\n","}\n","\n","/* Serial-specific style estimator block */\n","\n","#sk-container-id-4 div.sk-serial {\n","  display: flex;\n","  flex-direction: column;\n","  align-items: center;\n","  background-color: var(--sklearn-color-background);\n","  padding-right: 1em;\n","  padding-left: 1em;\n","}\n","\n","\n","/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n","clickable and can be expanded/collapsed.\n","- Pipeline and ColumnTransformer use this feature and define the default style\n","- Estimators will overwrite some part of the style using the `sk-estimator` class\n","*/\n","\n","/* Pipeline and ColumnTransformer style (default) */\n","\n","#sk-container-id-4 div.sk-toggleable {\n","  /* Default theme specific background. It is overwritten whether we have a\n","  specific estimator or a Pipeline/ColumnTransformer */\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","/* Toggleable label */\n","#sk-container-id-4 label.sk-toggleable__label {\n","  cursor: pointer;\n","  display: flex;\n","  width: 100%;\n","  margin-bottom: 0;\n","  padding: 0.5em;\n","  box-sizing: border-box;\n","  text-align: center;\n","  align-items: start;\n","  justify-content: space-between;\n","  gap: 0.5em;\n","}\n","\n","#sk-container-id-4 label.sk-toggleable__label .caption {\n","  font-size: 0.6rem;\n","  font-weight: lighter;\n","  color: var(--sklearn-color-text-muted);\n","}\n","\n","#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n","  /* Arrow on the left of the label */\n","  content: \"▸\";\n","  float: left;\n","  margin-right: 0.25em;\n","  color: var(--sklearn-color-icon);\n","}\n","\n","#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n","  color: var(--sklearn-color-text);\n","}\n","\n","/* Toggleable content - dropdown */\n","\n","#sk-container-id-4 div.sk-toggleable__content {\n","  max-height: 0;\n","  max-width: 0;\n","  overflow: hidden;\n","  text-align: left;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-4 div.sk-toggleable__content.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-4 div.sk-toggleable__content pre {\n","  margin: 0.2em;\n","  border-radius: 0.25em;\n","  color: var(--sklearn-color-text);\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n","  /* Expand drop-down */\n","  max-height: 200px;\n","  max-width: 100%;\n","  overflow: auto;\n","}\n","\n","#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n","  content: \"▾\";\n","}\n","\n","/* Pipeline/ColumnTransformer-specific style */\n","\n","#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator-specific style */\n","\n","/* Colorize estimator box */\n","#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n","#sk-container-id-4 div.sk-label label {\n","  /* The background is the default theme color */\n","  color: var(--sklearn-color-text-on-default-background);\n","}\n","\n","/* On hover, darken the color of the background */\n","#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","/* Label box, darken color on hover, fitted */\n","#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator label */\n","\n","#sk-container-id-4 div.sk-label label {\n","  font-family: monospace;\n","  font-weight: bold;\n","  display: inline-block;\n","  line-height: 1.2em;\n","}\n","\n","#sk-container-id-4 div.sk-label-container {\n","  text-align: center;\n","}\n","\n","/* Estimator-specific */\n","#sk-container-id-4 div.sk-estimator {\n","  font-family: monospace;\n","  border: 1px dotted var(--sklearn-color-border-box);\n","  border-radius: 0.25em;\n","  box-sizing: border-box;\n","  margin-bottom: 0.5em;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-4 div.sk-estimator.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","/* on hover */\n","#sk-container-id-4 div.sk-estimator:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-4 div.sk-estimator.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Specification for estimator info (e.g. \"i\" and \"?\") */\n","\n","/* Common style for \"i\" and \"?\" */\n","\n",".sk-estimator-doc-link,\n","a:link.sk-estimator-doc-link,\n","a:visited.sk-estimator-doc-link {\n","  float: right;\n","  font-size: smaller;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1em;\n","  height: 1em;\n","  width: 1em;\n","  text-decoration: none !important;\n","  margin-left: 0.5em;\n","  text-align: center;\n","  /* unfitted */\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-unfitted-level-1);\n","}\n","\n",".sk-estimator-doc-link.fitted,\n","a:link.sk-estimator-doc-link.fitted,\n","a:visited.sk-estimator-doc-link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","/* Span, style for the box shown on hovering the info icon */\n",".sk-estimator-doc-link span {\n","  display: none;\n","  z-index: 9999;\n","  position: relative;\n","  font-weight: normal;\n","  right: .2ex;\n","  padding: .5ex;\n","  margin: .5ex;\n","  width: min-content;\n","  min-width: 20ex;\n","  max-width: 50ex;\n","  color: var(--sklearn-color-text);\n","  box-shadow: 2pt 2pt 4pt #999;\n","  /* unfitted */\n","  background: var(--sklearn-color-unfitted-level-0);\n","  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n","}\n","\n",".sk-estimator-doc-link.fitted span {\n","  /* fitted */\n","  background: var(--sklearn-color-fitted-level-0);\n","  border: var(--sklearn-color-fitted-level-3);\n","}\n","\n",".sk-estimator-doc-link:hover span {\n","  display: block;\n","}\n","\n","/* \"?\"-specific style due to the `<a>` HTML tag */\n","\n","#sk-container-id-4 a.estimator_doc_link {\n","  float: right;\n","  font-size: 1rem;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1rem;\n","  height: 1rem;\n","  width: 1rem;\n","  text-decoration: none;\n","  /* unfitted */\n","  color: var(--sklearn-color-unfitted-level-1);\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","}\n","\n","#sk-container-id-4 a.estimator_doc_link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","#sk-container-id-4 a.estimator_doc_link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","}\n","</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n","                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n","                                                  [&#x27;lookback_days&#x27;,\n","                                                   &#x27;high_volatility&#x27;,\n","                                                   &#x27;technical_score&#x27;,\n","                                                   &#x27;edge_density&#x27;,\n","                                                   &#x27;slope_strength&#x27;,\n","                                                   &#x27;candlestick_variance&#x27;,\n","                                                   &#x27;pattern_symmetry&#x27;]),\n","                                                 (&#x27;cat&#x27;,\n","                                                  OneHotEncoder(drop=&#x27;first&#x27;),\n","                                                  [&#x27;asset_type&#x27;,\n","                                                   &#x27;market_regime&#x27;])])),\n","                (&#x27;classifier&#x27;,\n","                 LogisticRegression(max_iter=800, random_state=35))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n","                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n","                                                  [&#x27;lookback_days&#x27;,\n","                                                   &#x27;high_volatility&#x27;,\n","                                                   &#x27;technical_score&#x27;,\n","                                                   &#x27;edge_density&#x27;,\n","                                                   &#x27;slope_strength&#x27;,\n","                                                   &#x27;candlestick_variance&#x27;,\n","                                                   &#x27;pattern_symmetry&#x27;]),\n","                                                 (&#x27;cat&#x27;,\n","                                                  OneHotEncoder(drop=&#x27;first&#x27;),\n","                                                  [&#x27;asset_type&#x27;,\n","                                                   &#x27;market_regime&#x27;])])),\n","                (&#x27;classifier&#x27;,\n","                 LogisticRegression(max_iter=800, random_state=35))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>preprocessor: ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n","                                 [&#x27;lookback_days&#x27;, &#x27;high_volatility&#x27;,\n","                                  &#x27;technical_score&#x27;, &#x27;edge_density&#x27;,\n","                                  &#x27;slope_strength&#x27;, &#x27;candlestick_variance&#x27;,\n","                                  &#x27;pattern_symmetry&#x27;]),\n","                                (&#x27;cat&#x27;, OneHotEncoder(drop=&#x27;first&#x27;),\n","                                 [&#x27;asset_type&#x27;, &#x27;market_regime&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>num</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;lookback_days&#x27;, &#x27;high_volatility&#x27;, &#x27;technical_score&#x27;, &#x27;edge_density&#x27;, &#x27;slope_strength&#x27;, &#x27;candlestick_variance&#x27;, &#x27;pattern_symmetry&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>cat</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;asset_type&#x27;, &#x27;market_regime&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(drop=&#x27;first&#x27;)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=800, random_state=35)</pre></div> </div></div></div></div></div></div>"]},"metadata":{},"execution_count":48}]},{"cell_type":"code","source":["#building the neural network model\n","def build_model(input_shape):\n","    model = keras.Sequential([\n","        layers.Dense(128, activation='relu', input_shape=(input_shape,)),\n","        layers.Dense(64, activation='relu'),\n","        layers.Dense(1, activation='sigmoid')])\n","    loss_fn=keras.losses.BinaryCrossentropy()\n","    model.compile(optimizer='adam', loss=loss_fn, metrics=['accuracy'])\n","    return model"],"metadata":{"id":"aDTTCH2_z0FP"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#conversion of elements of dataset into tensors\n","x_train_processed = preprocess.fit_transform(x_train)\n","x_test_processed = preprocess.transform(x_test)\n","\n","x_train_tf = tf.convert_to_tensor(x_train_processed, dtype=tf.float32)\n","y_train_tf = tf.convert_to_tensor(y_train, dtype=tf.float32)\n","x_test_tf = tf.convert_to_tensor(x_test_processed, dtype=tf.float32)\n","y_test_tf = tf.convert_to_tensor(y_test, dtype=tf.float32)"],"metadata":{"id":"kbmbGZsz4MqL"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#creating model with the required number of features\n","model = build_model(x_train_processed.shape[1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DhDIR4ki8z6z","executionInfo":{"status":"ok","timestamp":1766429874171,"user_tz":-330,"elapsed":29,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"351726ec-3d36-4009-a423-9c454bd05f12"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:93: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n","  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]}]},{"cell_type":"code","source":["#fitting training samples in neural network model\n","model.fit(x_train_tf, y_train_tf, epochs=200, batch_size=32,validation_split=0.2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YJ-C9eu79aFL","executionInfo":{"status":"ok","timestamp":1766429901187,"user_tz":-330,"elapsed":24551,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"b647e925-dacc-4882-e5db-f155aae7f912"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 105ms/step - accuracy: 0.4437 - loss: 0.7158 - val_accuracy: 0.9062 - val_loss: 0.5753\n","Epoch 2/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.8625 - loss: 0.5455 - val_accuracy: 0.9062 - val_loss: 0.4783\n","Epoch 3/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.9125 - loss: 0.4468 - val_accuracy: 0.9062 - val_loss: 0.4162\n","Epoch 4/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.9396 - loss: 0.3479 - val_accuracy: 0.9062 - val_loss: 0.3785\n","Epoch 5/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.9302 - loss: 0.3081 - val_accuracy: 0.9062 - val_loss: 0.3533\n","Epoch 6/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.9479 - loss: 0.2499 - val_accuracy: 0.9062 - val_loss: 0.3381\n","Epoch 7/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - accuracy: 0.9104 - loss: 0.2967 - val_accuracy: 0.9062 - val_loss: 0.3202\n","Epoch 8/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.9417 - loss: 0.2119 - val_accuracy: 0.9062 - val_loss: 0.3071\n","Epoch 9/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.9365 - loss: 0.2228 - val_accuracy: 0.9062 - val_loss: 0.2924\n","Epoch 10/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - accuracy: 0.9229 - loss: 0.2318 - val_accuracy: 0.9062 - val_loss: 0.2740\n","Epoch 11/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.9500 - loss: 0.1579 - val_accuracy: 0.9062 - val_loss: 0.2586\n","Epoch 12/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - accuracy: 0.9177 - loss: 0.2193 - val_accuracy: 0.9062 - val_loss: 0.2410\n","Epoch 13/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.9354 - loss: 0.1814 - val_accuracy: 0.9062 - val_loss: 0.2284\n","Epoch 14/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9104 - loss: 0.2080 - val_accuracy: 0.9062 - val_loss: 0.2166\n","Epoch 15/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9500 - loss: 0.1332 - val_accuracy: 0.9062 - val_loss: 0.2104\n","Epoch 16/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 0.9677 - loss: 0.1133 - val_accuracy: 0.9062 - val_loss: 0.2065\n","Epoch 17/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9479 - loss: 0.1410 - val_accuracy: 0.9062 - val_loss: 0.2021\n","Epoch 18/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9552 - loss: 0.1208 - val_accuracy: 0.9062 - val_loss: 0.1986\n","Epoch 19/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9375 - loss: 0.1482 - val_accuracy: 0.9062 - val_loss: 0.1948\n","Epoch 20/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.9469 - loss: 0.1228 - val_accuracy: 0.9062 - val_loss: 0.1924\n","Epoch 21/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.9469 - loss: 0.1263 - val_accuracy: 0.9062 - val_loss: 0.1901\n","Epoch 22/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - accuracy: 0.9323 - loss: 0.1478 - val_accuracy: 0.9062 - val_loss: 0.1870\n","Epoch 23/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9302 - loss: 0.1552 - val_accuracy: 0.9062 - val_loss: 0.1849\n","Epoch 24/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9635 - loss: 0.1076 - val_accuracy: 0.9062 - val_loss: 0.1854\n","Epoch 25/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.9396 - loss: 0.1298 - val_accuracy: 0.9062 - val_loss: 0.1837\n","Epoch 26/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.9740 - loss: 0.0738 - val_accuracy: 0.9062 - val_loss: 0.1835\n","Epoch 27/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9542 - loss: 0.1133 - val_accuracy: 0.9062 - val_loss: 0.1808\n","Epoch 28/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.9354 - loss: 0.1240 - val_accuracy: 0.9062 - val_loss: 0.1771\n","Epoch 29/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9427 - loss: 0.1156 - val_accuracy: 0.9062 - val_loss: 0.1754\n","Epoch 30/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.9396 - loss: 0.1266 - val_accuracy: 0.9062 - val_loss: 0.1736\n","Epoch 31/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.9573 - loss: 0.1082 - val_accuracy: 0.9062 - val_loss: 0.1734\n","Epoch 32/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 0.9635 - loss: 0.0874 - val_accuracy: 0.9062 - val_loss: 0.1718\n","Epoch 33/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9594 - loss: 0.0995 - val_accuracy: 0.9062 - val_loss: 0.1714\n","Epoch 34/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9625 - loss: 0.0804 - val_accuracy: 0.9062 - val_loss: 0.1701\n","Epoch 35/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.9448 - loss: 0.1021 - val_accuracy: 0.9375 - val_loss: 0.1689\n","Epoch 36/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9688 - loss: 0.0798 - val_accuracy: 0.9375 - val_loss: 0.1690\n","Epoch 37/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9802 - loss: 0.0636 - val_accuracy: 0.9375 - val_loss: 0.1682\n","Epoch 38/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9719 - loss: 0.0696 - val_accuracy: 0.9375 - val_loss: 0.1661\n","Epoch 39/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.9781 - loss: 0.0656 - val_accuracy: 0.9375 - val_loss: 0.1642\n","Epoch 40/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9688 - loss: 0.0857 - val_accuracy: 0.9375 - val_loss: 0.1633\n","Epoch 41/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9688 - loss: 0.0752 - val_accuracy: 0.9375 - val_loss: 0.1608\n","Epoch 42/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - accuracy: 0.9646 - loss: 0.0671 - val_accuracy: 0.9375 - val_loss: 0.1604\n","Epoch 43/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9708 - loss: 0.0694 - val_accuracy: 0.9375 - val_loss: 0.1599\n","Epoch 44/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9729 - loss: 0.0611 - val_accuracy: 0.9375 - val_loss: 0.1585\n","Epoch 45/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.9833 - loss: 0.0493 - val_accuracy: 0.9375 - val_loss: 0.1585\n","Epoch 46/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.9625 - loss: 0.0721 - val_accuracy: 0.9375 - val_loss: 0.1549\n","Epoch 47/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.9885 - loss: 0.0585 - val_accuracy: 0.9375 - val_loss: 0.1543\n","Epoch 48/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.9771 - loss: 0.0636 - val_accuracy: 0.9375 - val_loss: 0.1521\n","Epoch 49/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9771 - loss: 0.0676 - val_accuracy: 0.9375 - val_loss: 0.1517\n","Epoch 50/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9865 - loss: 0.0536 - val_accuracy: 0.9375 - val_loss: 0.1514\n","Epoch 51/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9823 - loss: 0.0553 - val_accuracy: 0.9375 - val_loss: 0.1507\n","Epoch 52/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.9917 - loss: 0.0406 - val_accuracy: 0.9375 - val_loss: 0.1498\n","Epoch 53/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9896 - loss: 0.0394 - val_accuracy: 0.9375 - val_loss: 0.1501\n","Epoch 54/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9802 - loss: 0.0486 - val_accuracy: 0.9375 - val_loss: 0.1457\n","Epoch 55/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 0.9708 - loss: 0.0572 - val_accuracy: 0.9375 - val_loss: 0.1445\n","Epoch 56/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 0.9937 - loss: 0.0395 - val_accuracy: 0.9375 - val_loss: 0.1464\n","Epoch 57/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.9802 - loss: 0.0465 - val_accuracy: 0.9375 - val_loss: 0.1454\n","Epoch 58/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.9865 - loss: 0.0464 - val_accuracy: 0.9375 - val_loss: 0.1464\n","Epoch 59/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.9937 - loss: 0.0285 - val_accuracy: 0.9375 - val_loss: 0.1471\n","Epoch 60/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.9771 - loss: 0.0489 - val_accuracy: 0.9375 - val_loss: 0.1428\n","Epoch 61/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.9969 - loss: 0.0339 - val_accuracy: 0.9375 - val_loss: 0.1417\n","Epoch 62/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.9917 - loss: 0.0336 - val_accuracy: 0.9375 - val_loss: 0.1417\n","Epoch 63/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9917 - loss: 0.0383 - val_accuracy: 0.9375 - val_loss: 0.1411\n","Epoch 64/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 0.9969 - loss: 0.0270 - val_accuracy: 0.9375 - val_loss: 0.1413\n","Epoch 65/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.9917 - loss: 0.0256 - val_accuracy: 0.9375 - val_loss: 0.1410\n","Epoch 66/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 0.9917 - loss: 0.0306 - val_accuracy: 0.9375 - val_loss: 0.1416\n","Epoch 67/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0360 - val_accuracy: 0.9375 - val_loss: 0.1384\n","Epoch 68/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 1.0000 - loss: 0.0276 - val_accuracy: 0.9375 - val_loss: 0.1368\n","Epoch 69/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0348 - val_accuracy: 0.9375 - val_loss: 0.1362\n","Epoch 70/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0269 - val_accuracy: 0.9375 - val_loss: 0.1361\n","Epoch 71/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0225 - val_accuracy: 0.9375 - val_loss: 0.1383\n","Epoch 72/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 1.0000 - loss: 0.0241 - val_accuracy: 0.9375 - val_loss: 0.1408\n","Epoch 73/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0207 - val_accuracy: 0.9375 - val_loss: 0.1395\n","Epoch 74/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0239 - val_accuracy: 0.9375 - val_loss: 0.1364\n","Epoch 75/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0224 - val_accuracy: 0.9375 - val_loss: 0.1362\n","Epoch 76/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 1.0000 - loss: 0.0301 - val_accuracy: 0.9375 - val_loss: 0.1362\n","Epoch 77/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0255 - val_accuracy: 0.9375 - val_loss: 0.1342\n","Epoch 78/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 1.0000 - loss: 0.0183 - val_accuracy: 0.9375 - val_loss: 0.1388\n","Epoch 79/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0224 - val_accuracy: 0.9375 - val_loss: 0.1354\n","Epoch 80/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0200 - val_accuracy: 0.9375 - val_loss: 0.1388\n","Epoch 81/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0221 - val_accuracy: 0.9375 - val_loss: 0.1373\n","Epoch 82/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 0.0136 - val_accuracy: 0.9375 - val_loss: 0.1411\n","Epoch 83/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0136 - val_accuracy: 0.9375 - val_loss: 0.1397\n","Epoch 84/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0151 - val_accuracy: 0.9375 - val_loss: 0.1351\n","Epoch 85/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0155 - val_accuracy: 0.9375 - val_loss: 0.1387\n","Epoch 86/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0099 - val_accuracy: 0.9375 - val_loss: 0.1400\n","Epoch 87/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0184 - val_accuracy: 0.9375 - val_loss: 0.1382\n","Epoch 88/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0119 - val_accuracy: 0.9375 - val_loss: 0.1393\n","Epoch 89/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0168 - val_accuracy: 0.9375 - val_loss: 0.1395\n","Epoch 90/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 1.0000 - loss: 0.0099 - val_accuracy: 0.9375 - val_loss: 0.1386\n","Epoch 91/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0133 - val_accuracy: 0.9375 - val_loss: 0.1387\n","Epoch 92/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 1.0000 - loss: 0.0171 - val_accuracy: 0.9375 - val_loss: 0.1411\n","Epoch 93/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0121 - val_accuracy: 0.9375 - val_loss: 0.1441\n","Epoch 94/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0106 - val_accuracy: 0.9375 - val_loss: 0.1419\n","Epoch 95/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0095 - val_accuracy: 0.9375 - val_loss: 0.1421\n","Epoch 96/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 1.0000 - loss: 0.0086 - val_accuracy: 0.9375 - val_loss: 0.1417\n","Epoch 97/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0092 - val_accuracy: 0.9375 - val_loss: 0.1398\n","Epoch 98/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 1.0000 - loss: 0.0120 - val_accuracy: 0.9375 - val_loss: 0.1426\n","Epoch 99/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0093 - val_accuracy: 0.9375 - val_loss: 0.1434\n","Epoch 100/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0095 - val_accuracy: 0.9375 - val_loss: 0.1439\n","Epoch 101/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0101 - val_accuracy: 0.9375 - val_loss: 0.1442\n","Epoch 102/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 0.0119 - val_accuracy: 0.9375 - val_loss: 0.1451\n","Epoch 103/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0092 - val_accuracy: 0.9375 - val_loss: 0.1497\n","Epoch 104/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0111 - val_accuracy: 0.9375 - val_loss: 0.1459\n","Epoch 105/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0078 - val_accuracy: 0.9375 - val_loss: 0.1445\n","Epoch 106/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0067 - val_accuracy: 0.9375 - val_loss: 0.1470\n","Epoch 107/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 0.0087 - val_accuracy: 0.9375 - val_loss: 0.1487\n","Epoch 108/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 1.0000 - loss: 0.0085 - val_accuracy: 0.9375 - val_loss: 0.1503\n","Epoch 109/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - accuracy: 1.0000 - loss: 0.0071 - val_accuracy: 0.9375 - val_loss: 0.1503\n","Epoch 110/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - accuracy: 1.0000 - loss: 0.0073 - val_accuracy: 0.9375 - val_loss: 0.1444\n","Epoch 111/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 0.0069 - val_accuracy: 0.9375 - val_loss: 0.1457\n","Epoch 112/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 0.0074 - val_accuracy: 0.9375 - val_loss: 0.1518\n","Epoch 113/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 1.0000 - loss: 0.0088 - val_accuracy: 0.9375 - val_loss: 0.1558\n","Epoch 114/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - accuracy: 1.0000 - loss: 0.0054 - val_accuracy: 0.9375 - val_loss: 0.1563\n","Epoch 115/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 1.0000 - loss: 0.0052 - val_accuracy: 0.9375 - val_loss: 0.1538\n","Epoch 116/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - accuracy: 1.0000 - loss: 0.0070 - val_accuracy: 0.9375 - val_loss: 0.1535\n","Epoch 117/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - accuracy: 1.0000 - loss: 0.0068 - val_accuracy: 0.9375 - val_loss: 0.1558\n","Epoch 118/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - accuracy: 1.0000 - loss: 0.0041 - val_accuracy: 0.9375 - val_loss: 0.1581\n","Epoch 119/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0050 - val_accuracy: 0.9375 - val_loss: 0.1572\n","Epoch 120/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0044 - val_accuracy: 0.9375 - val_loss: 0.1555\n","Epoch 121/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0045 - val_accuracy: 0.9375 - val_loss: 0.1552\n","Epoch 122/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 1.0000 - loss: 0.0070 - val_accuracy: 0.9375 - val_loss: 0.1586\n","Epoch 123/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0051 - val_accuracy: 0.9375 - val_loss: 0.1600\n","Epoch 124/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0041 - val_accuracy: 0.9375 - val_loss: 0.1607\n","Epoch 125/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0040 - val_accuracy: 0.9375 - val_loss: 0.1575\n","Epoch 126/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0038 - val_accuracy: 0.9375 - val_loss: 0.1619\n","Epoch 127/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0046 - val_accuracy: 0.9375 - val_loss: 0.1626\n","Epoch 128/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0052 - val_accuracy: 0.9375 - val_loss: 0.1625\n","Epoch 129/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 0.0044 - val_accuracy: 0.9375 - val_loss: 0.1595\n","Epoch 130/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0049 - val_accuracy: 0.9375 - val_loss: 0.1606\n","Epoch 131/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0037 - val_accuracy: 0.9375 - val_loss: 0.1633\n","Epoch 132/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 1.0000 - loss: 0.0039 - val_accuracy: 0.9375 - val_loss: 0.1617\n","Epoch 133/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0048 - val_accuracy: 0.9375 - val_loss: 0.1613\n","Epoch 134/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0043 - val_accuracy: 0.9375 - val_loss: 0.1655\n","Epoch 135/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0049 - val_accuracy: 0.9375 - val_loss: 0.1681\n","Epoch 136/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0041 - val_accuracy: 0.9375 - val_loss: 0.1648\n","Epoch 137/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0033 - val_accuracy: 0.9375 - val_loss: 0.1650\n","Epoch 138/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0033 - val_accuracy: 0.9375 - val_loss: 0.1673\n","Epoch 139/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0039 - val_accuracy: 0.9375 - val_loss: 0.1690\n","Epoch 140/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0025 - val_accuracy: 0.9375 - val_loss: 0.1721\n","Epoch 141/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0037 - val_accuracy: 0.9375 - val_loss: 0.1723\n","Epoch 142/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0038 - val_accuracy: 0.9375 - val_loss: 0.1708\n","Epoch 143/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0038 - val_accuracy: 0.9375 - val_loss: 0.1673\n","Epoch 144/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0023 - val_accuracy: 0.9375 - val_loss: 0.1691\n","Epoch 145/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0033 - val_accuracy: 0.9375 - val_loss: 0.1700\n","Epoch 146/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0030 - val_accuracy: 0.9375 - val_loss: 0.1726\n","Epoch 147/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0029 - val_accuracy: 0.9062 - val_loss: 0.1745\n","Epoch 148/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0035 - val_accuracy: 0.9375 - val_loss: 0.1727\n","Epoch 149/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0023 - val_accuracy: 0.9062 - val_loss: 0.1771\n","Epoch 150/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0030 - val_accuracy: 0.9062 - val_loss: 0.1786\n","Epoch 151/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 1.0000 - loss: 0.0026 - val_accuracy: 0.9062 - val_loss: 0.1779\n","Epoch 152/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 0.0019 - val_accuracy: 0.9062 - val_loss: 0.1757\n","Epoch 153/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0028 - val_accuracy: 0.9375 - val_loss: 0.1731\n","Epoch 154/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0024 - val_accuracy: 0.9062 - val_loss: 0.1748\n","Epoch 155/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0020 - val_accuracy: 0.9062 - val_loss: 0.1803\n","Epoch 156/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0026 - val_accuracy: 0.9062 - val_loss: 0.1819\n","Epoch 157/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0026 - val_accuracy: 0.9062 - val_loss: 0.1816\n","Epoch 158/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0020 - val_accuracy: 0.9062 - val_loss: 0.1818\n","Epoch 159/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0021 - val_accuracy: 0.9062 - val_loss: 0.1820\n","Epoch 160/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 0.0019 - val_accuracy: 0.9062 - val_loss: 0.1805\n","Epoch 161/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0027 - val_accuracy: 0.9062 - val_loss: 0.1815\n","Epoch 162/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0023 - val_accuracy: 0.9062 - val_loss: 0.1822\n","Epoch 163/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0023 - val_accuracy: 0.9062 - val_loss: 0.1845\n","Epoch 164/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0018 - val_accuracy: 0.9062 - val_loss: 0.1846\n","Epoch 165/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0017 - val_accuracy: 0.9062 - val_loss: 0.1852\n","Epoch 166/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0018 - val_accuracy: 0.9062 - val_loss: 0.1869\n","Epoch 167/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0017 - val_accuracy: 0.9062 - val_loss: 0.1858\n","Epoch 168/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0018 - val_accuracy: 0.9062 - val_loss: 0.1854\n","Epoch 169/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0013 - val_accuracy: 0.9062 - val_loss: 0.1877\n","Epoch 170/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0017 - val_accuracy: 0.9062 - val_loss: 0.1889\n","Epoch 171/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0016 - val_accuracy: 0.9062 - val_loss: 0.1876\n","Epoch 172/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0014 - val_accuracy: 0.9062 - val_loss: 0.1873\n","Epoch 173/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0012 - val_accuracy: 0.9062 - val_loss: 0.1892\n","Epoch 174/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0020 - val_accuracy: 0.9062 - val_loss: 0.1895\n","Epoch 175/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0013 - val_accuracy: 0.9062 - val_loss: 0.1906\n","Epoch 176/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0013 - val_accuracy: 0.9062 - val_loss: 0.1929\n","Epoch 177/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0014 - val_accuracy: 0.9062 - val_loss: 0.1932\n","Epoch 178/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0015 - val_accuracy: 0.9062 - val_loss: 0.1929\n","Epoch 179/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0021 - val_accuracy: 0.9062 - val_loss: 0.1941\n","Epoch 180/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - accuracy: 1.0000 - loss: 0.0018 - val_accuracy: 0.9062 - val_loss: 0.1936\n","Epoch 181/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0017 - val_accuracy: 0.9062 - val_loss: 0.1960\n","Epoch 182/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0013 - val_accuracy: 0.9062 - val_loss: 0.1969\n","Epoch 183/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 0.0014 - val_accuracy: 0.9062 - val_loss: 0.1976\n","Epoch 184/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0014 - val_accuracy: 0.9062 - val_loss: 0.1970\n","Epoch 185/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 1.0000 - loss: 9.4105e-04 - val_accuracy: 0.9062 - val_loss: 0.1973\n","Epoch 186/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0013 - val_accuracy: 0.9062 - val_loss: 0.1986\n","Epoch 187/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0018 - val_accuracy: 0.9062 - val_loss: 0.1996\n","Epoch 188/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 0.0015 - val_accuracy: 0.9062 - val_loss: 0.2000\n","Epoch 189/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 1.0000 - loss: 0.0015 - val_accuracy: 0.9062 - val_loss: 0.1993\n","Epoch 190/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 9.1361e-04 - val_accuracy: 0.9062 - val_loss: 0.2015\n","Epoch 191/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0010 - val_accuracy: 0.9062 - val_loss: 0.2057\n","Epoch 192/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0016 - val_accuracy: 0.9062 - val_loss: 0.2025\n","Epoch 193/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 0.0012 - val_accuracy: 0.9062 - val_loss: 0.2009\n","Epoch 194/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 9.8884e-04 - val_accuracy: 0.9062 - val_loss: 0.2008\n","Epoch 195/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0010 - val_accuracy: 0.9062 - val_loss: 0.2027\n","Epoch 196/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 0.0013 - val_accuracy: 0.9062 - val_loss: 0.2078\n","Epoch 197/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0011 - val_accuracy: 0.9062 - val_loss: 0.2060\n","Epoch 198/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0014 - val_accuracy: 0.9062 - val_loss: 0.2053\n","Epoch 199/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 0.0012 - val_accuracy: 0.9062 - val_loss: 0.2073\n","Epoch 200/200\n","\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 0.0010 - val_accuracy: 0.9062 - val_loss: 0.2085\n"]},{"output_type":"execute_result","data":{"text/plain":["<keras.src.callbacks.history.History at 0x78ccaef2f740>"]},"metadata":{},"execution_count":52}]},{"cell_type":"code","source":["print(\"NEURAL NETWORK MODEL EVALUATION:\\n\")\n","# Predict probabilities\n","y_pred_prob=model.predict(x_test_tf)\n","# Convert probabilities to binary labels (0/1)\n","y_pred=(y_pred_prob >= 0.5).astype(int).reshape(-1)\n","y_true=y_test_tf.numpy()\n","accuracy=accuracy_score(y_true, y_pred)\n","precision=precision_score(y_true, y_pred)\n","recall=recall_score(y_true, y_pred)\n","f1=f1_score(y_true, y_pred)\n","cm=confusion_matrix(y_true, y_pred)\n","print(\"Accuracy:\", accuracy)\n","print(\"Precision:\", precision)\n","print(\"Recall:\", recall)\n","print(\"F1-score:\", f1)\n","print(\"\\nConfusion Matrix:\")\n","print(cm)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zjOD0WBYAiy2","executionInfo":{"status":"ok","timestamp":1766429902660,"user_tz":-330,"elapsed":253,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"5a23bf9e-d6fa-4397-85cc-2df1a0ee9c93"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["NEURAL NETWORK MODEL EVALUATION:\n","\n","\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step\n","Accuracy: 0.95\n","Precision: 0.9736842105263158\n","Recall: 0.9736842105263158\n","F1-score: 0.9736842105263158\n","\n","Confusion Matrix:\n","[[ 1  1]\n"," [ 1 37]]\n"]}]},{"cell_type":"markdown","source":[],"metadata":{"id":"I55sa7lPFU0S"}},{"cell_type":"code","source":["print(\"LOGISTIC REGRESSION MODEL EVALUATION:\")\n","y_pred=clf.predict(x_test)\n","accuracy=accuracy_score(y_test, y_pred)\n","precision=precision_score(y_test, y_pred)\n","recall=recall_score(y_test, y_pred)\n","f1=f1_score(y_test, y_pred)\n","cm=confusion_matrix(y_test, y_pred)\n","print(\"Accuracy:\", accuracy)\n","print(\"Precision:\", precision)\n","print(\"Recall:\", recall)\n","print(\"F1-score:\", f1)\n","print(\"\\nConfusion Matrix:\")\n","print(cm)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MPgtYH2GW1J0","executionInfo":{"status":"ok","timestamp":1766429907468,"user_tz":-330,"elapsed":16,"user":{"displayName":"Saksham Agarwal","userId":"14846103380388986571"}},"outputId":"48d0024b-e8af-4689-b3e2-e96f65b684bc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["LOGISTIC REGRESSION MODEL EVALUATION:\n","Accuracy: 0.925\n","Precision: 0.9487179487179487\n","Recall: 0.9736842105263158\n","F1-score: 0.961038961038961\n","\n","Confusion Matrix:\n","[[ 0  2]\n"," [ 1 37]]\n"]}]},{"cell_type":"markdown","source":["***ANALYSIS AND FINANCIAL INTERPRETATION***\n","1. Logistic Regression uses a linear decision boundary but financial markets do not assume a linear decision boundary so it performs well only to a certain extent.\n","2. Neural Networks can work with non-linear relations well so we get better results. This is quite evident from the respective accuracies that we have calculated.\n","3. High volatility leads to failure in making the correct predictions because in these cases the market can respond as bullish or bearish, it is difficult to conclude.\n","4. Trend continuation makes it easy for our model to make the correct prediction.Hence it helps in the achieving high accuracy.\n","5. In case of high volatility, sideways market, candlesticks we are not familiar with , the model fails to take the right decision. Financial markets are affected by external events, some company related news but our model only relies on the statistics and does not understand the sentiments of the people."],"metadata":{"id":"KEhsb1wScAer"}}]}
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