diff --git a/Assignment1_dwij_240381.ipynb b/Assignment1_dwij_240381.ipynb
new file mode 100644
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--- /dev/null
+++ b/Assignment1_dwij_240381.ipynb
@@ -0,0 +1,545 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "FflvZ7-QdB_N",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "108a036f-12d3-4f85-dd01-ccd1c0d0e181"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "15 is odd\n",
+            "22 is even\n",
+            "7 is odd\n",
+            "31 is odd\n",
+            "4 is even\n",
+            "17 is odd\n",
+            "[22, 4]\n",
+            "[225, 49, 961, 289]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "numbers = [15,22,7,31,4,17]\n",
+        "\n",
+        "for n in numbers:\n",
+        "  if n % 2 == 0:\n",
+        "    print(n, \"is even\")\n",
+        "  else:\n",
+        "    print(n, \"is odd\")\n",
+        "\n",
+        "new_list = [n for n in numbers if n % 2 == 0]\n",
+        "print(new_list)\n",
+        "new_list_2 = [n**2 for n in numbers if n % 2 != 0]\n",
+        "print(new_list_2)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "sentence = \"The book was interesting because the book covered many topics and the topics discussed in the book were engaging\"\n",
+        "words = sentence.lower().split()\n",
+        "\n",
+        "word_count = {}\n",
+        "for word in words:\n",
+        "  if word in word_count:\n",
+        "    word_count[word] += 1\n",
+        "  else:\n",
+        "    word_count[word] = 1\n",
+        "\n",
+        "print(\"Word counts:\", word_count)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "e2AN2MAd-_YS",
+        "outputId": "2c8fbf45-2f15-43a6-922d-8fb487a0ea93"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Word counts: {'the': 4, 'book': 3, 'was': 1, 'interesting': 1, 'because': 1, 'covered': 1, 'many': 1, 'topics': 2, 'and': 1, 'discussed': 1, 'in': 1, 'were': 1, 'engaging': 1}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def is_prime(n):\n",
+        "  if n<2:\n",
+        "    return False\n",
+        "  for i in range(2, int(n**0.5) + 1):\n",
+        "    if n % i == 0:\n",
+        "      return False\n",
+        "  return True\n",
+        "\n",
+        "\n",
+        "\n",
+        "nums = [2, 4, 5, 10, 13, 17, 20, 23]\n",
+        "prime_nums = [n for n in nums if is_prime(n)]\n",
+        "print(\"Prime numbers:\", prime_nums)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XP6yXk9nGDrU",
+        "outputId": "b2cc37b6-1599-428f-e298-865019e9a02f"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Prime numbers: [2, 5, 13, 17, 23]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "np.random.seed(0)\n",
+        "scores = np.random.randint(50, 101, size=(10, 5))\n",
+        "avg_scores = scores.mean(axis=1).reshape(-1, 1)\n",
+        "final_array = np.hstack((scores, avg_scores))\n",
+        "\n",
+        "print(\"Final Scores with Averages:\\n\", final_array)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lXtsdxzFHUTC",
+        "outputId": "259cf7dd-4540-4f09-b12b-eb40d82d5799"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Final Scores with Averages:\n",
+            " [[ 94.   97.   50.   53.   53.   69.4]\n",
+            " [ 89.   59.   69.   71.  100.   77.6]\n",
+            " [ 86.   73.   56.   74.   74.   72.6]\n",
+            " [ 62.   51.   88.   89.   73.   72.6]\n",
+            " [ 96.   74.   67.   87.   75.   79.8]\n",
+            " [ 63.   58.   59.   70.   66.   63.2]\n",
+            " [ 55.   65.   97.   50.   68.   67. ]\n",
+            " [ 85.   74.   99.   79.   69.   81.2]\n",
+            " [ 69.   64.   89.   82.   51.   71. ]\n",
+            " [ 59.   82.   81.   60.   73.   71. ]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "columns = [\"Subject1\", \"Subject2\", \"Subject3\", \"Subject4\", \"Subject5\", \"Average\"]\n",
+        "df_scores = pd.DataFrame(final_array, columns=columns)\n",
+        "df_scores.to_csv(\"scores.csv\", index=False)\n"
+      ],
+      "metadata": {
+        "id": "Urc3sYRiWsyN"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "data = {\n",
+        "    \"Product\": [\"Pen\", \"Notebook\", \"Pencil\", \"Eraser\", \"Marker\", \"Stapler\", \"Folder\", \"Tape\"],\n",
+        "    \"Price\": [7.0, 17.0, 20.0, 53.0, 12.0, 27.0, 11.0, 9.0],\n",
+        "    \"Quantity\": [12, 6, 26, 17, 8, 3, 14, 6]\n",
+        "}\n",
+        "df = pd.DataFrame(data)\n",
+        "df[\"TotalValue\"] = df[\"Price\"] * df[\"Quantity\"]\n",
+        "\n",
+        "df_sorted = df.sort_values(by=\"TotalValue\", ascending=False)\n",
+        "print(df_sorted)\n",
+        "\n",
+        "grouped_avg = df.groupby(\"Price\")[\"Quantity\"].mean()\n",
+        "print(grouped_avg)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vlsXps1xXC5h",
+        "outputId": "5caedef7-7255-4244-a7d5-16dbb719f97a"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "    Product  Price  Quantity  TotalValue\n",
+            "3    Eraser   53.0        17       901.0\n",
+            "2    Pencil   20.0        26       520.0\n",
+            "6    Folder   11.0        14       154.0\n",
+            "1  Notebook   17.0         6       102.0\n",
+            "4    Marker   12.0         8        96.0\n",
+            "0       Pen    7.0        12        84.0\n",
+            "5   Stapler   27.0         3        81.0\n",
+            "7      Tape    9.0         6        54.0\n",
+            "Price\n",
+            "7.0     12.0\n",
+            "9.0      6.0\n",
+            "11.0    14.0\n",
+            "12.0     8.0\n",
+            "17.0     6.0\n",
+            "20.0    26.0\n",
+            "27.0     3.0\n",
+            "53.0    17.0\n",
+            "Name: Quantity, dtype: float64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "titanic = pd.read_csv(\"train.csv\")\n",
+        "\n",
+        "print(\"Missing values:\\n\", titanic.isnull().sum())\n",
+        "\n",
+        "under_18 = titanic[titanic[\"Age\"] < 18]\n",
+        "print(\"Passengers under 18:\\n\", under_18)\n",
+        "\n",
+        "female_survivors = titanic[(titanic[\"Sex\"] == \"female\") & (titanic[\"Survived\"] == 1)]\n",
+        "print(\"Female survivors:\\n\", female_survivors)\n",
+        "\n",
+        "avg_fare_by_class = titanic.groupby(\"Pclass\")[\"Fare\"].mean()\n",
+        "print(\"Average fare by class:\\n\", avg_fare_by_class)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BxAmjZQmA6BM",
+        "outputId": "492b5468-ac21-4e8d-c409-8ea434cf041b"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Missing values:\n",
+            " PassengerId      0\n",
+            "Survived         0\n",
+            "Pclass           0\n",
+            "Name             0\n",
+            "Sex              0\n",
+            "Age            177\n",
+            "SibSp            0\n",
+            "Parch            0\n",
+            "Ticket           0\n",
+            "Fare             0\n",
+            "Cabin          687\n",
+            "Embarked         2\n",
+            "dtype: int64\n",
+            "Passengers under 18:\n",
+            "      PassengerId  Survived  Pclass                                     Name  \\\n",
+            "7              8         0       3           Palsson, Master. Gosta Leonard   \n",
+            "9             10         1       2      Nasser, Mrs. Nicholas (Adele Achem)   \n",
+            "10            11         1       3          Sandstrom, Miss. Marguerite Rut   \n",
+            "14            15         0       3     Vestrom, Miss. Hulda Amanda Adolfina   \n",
+            "16            17         0       3                     Rice, Master. Eugene   \n",
+            "..           ...       ...     ...                                      ...   \n",
+            "850          851         0       3  Andersson, Master. Sigvard Harald Elias   \n",
+            "852          853         0       3                  Boulos, Miss. Nourelain   \n",
+            "853          854         1       1                Lines, Miss. Mary Conover   \n",
+            "869          870         1       3          Johnson, Master. Harold Theodor   \n",
+            "875          876         1       3         Najib, Miss. Adele Kiamie \"Jane\"   \n",
+            "\n",
+            "        Sex   Age  SibSp  Parch    Ticket     Fare Cabin Embarked  \n",
+            "7      male   2.0      3      1    349909  21.0750   NaN        S  \n",
+            "9    female  14.0      1      0    237736  30.0708   NaN        C  \n",
+            "10   female   4.0      1      1   PP 9549  16.7000    G6        S  \n",
+            "14   female  14.0      0      0    350406   7.8542   NaN        S  \n",
+            "16     male   2.0      4      1    382652  29.1250   NaN        Q  \n",
+            "..      ...   ...    ...    ...       ...      ...   ...      ...  \n",
+            "850    male   4.0      4      2    347082  31.2750   NaN        S  \n",
+            "852  female   9.0      1      1      2678  15.2458   NaN        C  \n",
+            "853  female  16.0      0      1  PC 17592  39.4000   D28        S  \n",
+            "869    male   4.0      1      1    347742  11.1333   NaN        S  \n",
+            "875  female  15.0      0      0      2667   7.2250   NaN        C  \n",
+            "\n",
+            "[113 rows x 12 columns]\n",
+            "Female survivors:\n",
+            "      PassengerId  Survived  Pclass  \\\n",
+            "1              2         1       1   \n",
+            "2              3         1       3   \n",
+            "3              4         1       1   \n",
+            "8              9         1       3   \n",
+            "9             10         1       2   \n",
+            "..           ...       ...     ...   \n",
+            "874          875         1       2   \n",
+            "875          876         1       3   \n",
+            "879          880         1       1   \n",
+            "880          881         1       2   \n",
+            "887          888         1       1   \n",
+            "\n",
+            "                                                  Name     Sex   Age  SibSp  \\\n",
+            "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+            "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
+            "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+            "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
+            "9                  Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
+            "..                                                 ...     ...   ...    ...   \n",
+            "874              Abelson, Mrs. Samuel (Hannah Wizosky)  female  28.0      1   \n",
+            "875                   Najib, Miss. Adele Kiamie \"Jane\"  female  15.0      0   \n",
+            "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  56.0      0   \n",
+            "880       Shelley, Mrs. William (Imanita Parrish Hall)  female  25.0      0   \n",
+            "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
+            "\n",
+            "     Parch            Ticket     Fare Cabin Embarked  \n",
+            "1        0          PC 17599  71.2833   C85        C  \n",
+            "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
+            "3        0            113803  53.1000  C123        S  \n",
+            "8        2            347742  11.1333   NaN        S  \n",
+            "9        0            237736  30.0708   NaN        C  \n",
+            "..     ...               ...      ...   ...      ...  \n",
+            "874      0         P/PP 3381  24.0000   NaN        C  \n",
+            "875      0              2667   7.2250   NaN        C  \n",
+            "879      1             11767  83.1583   C50        C  \n",
+            "880      1            230433  26.0000   NaN        S  \n",
+            "887      0            112053  30.0000   B42        S  \n",
+            "\n",
+            "[233 rows x 12 columns]\n",
+            "Average fare by class:\n",
+            " Pclass\n",
+            "1    84.154687\n",
+            "2    20.662183\n",
+            "3    13.675550\n",
+            "Name: Fare, dtype: float64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "heart = pd.read_csv(\"processed.cleveland.data\", header=None, na_values='?')\n",
+        "heart.columns = ['Age', 'Sex', 'ChestPainType', 'RestingBP', 'Cholesterol', 'FastingBS',\n",
+        "                 'RestingECG', 'MaxHR', 'ExerciseAngina', 'Oldpeak', 'ST_Slope',\n",
+        "                 'NumVesselsFluoro', 'Thalassemia', 'HeartDisease']\n",
+        "\n",
+        "# Convert to numeric\n",
+        "cols_to_numeric = ['NumVesselsFluoro', 'Thalassemia']\n",
+        "heart[cols_to_numeric] = heart[cols_to_numeric].apply(pd.to_numeric, errors='coerce')\n",
+        "\n",
+        "print(\"Missing values:\\n\", heart.isnull().sum())\n",
+        "\n",
+        "# Impute missing values\n",
+        "for col in ['NumVesselsFluoro', 'Thalassemia']:\n",
+        "    if heart[col].dtype == 'float64':\n",
+        "        heart[col].fillna(heart[col].median(), inplace=True)\n",
+        "    else:\n",
+        "        heart[col].fillna(heart[col].mode()[0], inplace=True)\n",
+        "\n",
+        "# Drop rows with >2 missing\n",
+        "heart = heart[heart.isnull().sum(axis=1) <= 2]\n",
+        "\n",
+        "# Group by HeartDisease\n",
+        "chol_avg = heart.groupby(\"HeartDisease\")[\"Cholesterol\"].mean()\n",
+        "print(\"Average Cholesterol by Heart Disease:\\n\", chol_avg)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oCgtjvFTBhVn",
+        "outputId": "68bf8048-4baa-4644-9b8a-20bf055097cc"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Missing values:\n",
+            " Age                 0\n",
+            "Sex                 0\n",
+            "ChestPainType       0\n",
+            "RestingBP           0\n",
+            "Cholesterol         0\n",
+            "FastingBS           0\n",
+            "RestingECG          0\n",
+            "MaxHR               0\n",
+            "ExerciseAngina      0\n",
+            "Oldpeak             0\n",
+            "ST_Slope            0\n",
+            "NumVesselsFluoro    4\n",
+            "Thalassemia         2\n",
+            "HeartDisease        0\n",
+            "dtype: int64\n",
+            "Average Cholesterol by Heart Disease:\n",
+            " HeartDisease\n",
+            "0    242.640244\n",
+            "1    249.109091\n",
+            "2    259.277778\n",
+            "3    246.457143\n",
+            "4    253.384615\n",
+            "Name: Cholesterol, dtype: float64\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-9-a26ae98f4b3d>:15: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
+            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
+            "\n",
+            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
+            "\n",
+            "\n",
+            "  heart[col].fillna(heart[col].median(), inplace=True)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "data = {\n",
+        "    \"Name\": [\"name_1\", \"name_2\", \"name_3\", \"name_4\", \"name_5\"],\n",
+        "    \"Math\": [85, 78, 92, 88, 76],\n",
+        "    \"Science\": [90, 82, 89, 94, 75],\n",
+        "    \"English\": [78, 85, 88, 80, 82],\n",
+        "    \"History\": [84, 80, 91, 79, 77]\n",
+        "}\n",
+        "df_scores = pd.DataFrame(data)\n",
+        "\n",
+        "plt.figure(figsize=(6, 4))\n",
+        "plt.bar(df_scores[\"Name\"], df_scores[\"Math\"], color='skyblue')\n",
+        "plt.title(\"Math Scores\")\n",
+        "plt.xlabel(\"Student\")\n",
+        "plt.ylabel(\"Score\")\n",
+        "plt.show()\n",
+        "\n",
+        "plt.figure(figsize=(8, 5))\n",
+        "for subject in [\"Math\", \"Science\", \"English\", \"History\"]:\n",
+        "    plt.plot(df_scores[\"Name\"], df_scores[subject], label=subject)\n",
+        "\n",
+        "plt.title(\"Student Performance Across Subjects\")\n",
+        "plt.xlabel(\"Student\")\n",
+        "plt.ylabel(\"Scores\")\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 880
+        },
+        "id": "nraT967XbYRE",
+        "outputId": "088bca3f-4a05-49f8-d5c4-e403c1153d05"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data = np.random.normal(loc=50, scale=10, size=1000)\n",
+        "\n",
+        "plt.figure(figsize=(7, 4))\n",
+        "plt.hist(data, bins=20, color='blue', edgecolor='black')\n",
+        "plt.axvline(data.mean(), color='red', linewidth=1)\n",
+        "plt.title(\"Histogram of Normally Distributed Data\")\n",
+        "plt.xlabel(\"Value\")\n",
+        "plt.ylabel(\"Frequency\")\n",
+        "plt.grid(True)\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 410
+        },
+        "id": "j8SyXj71CH4n",
+        "outputId": "6c6f77aa-b4f6-428b-e1d2-fc42eccca11e"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 700x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file