diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 8a9fa9e..7c8d48f 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -12,11 +12,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 175,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Import your libraries:\n"
+    "# Import your libraries:\n",
+    "import pandas as pd\n",
+    "import numpy as np"
    ]
   },
   {
@@ -37,11 +39,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 176,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "from sklearn.datasets import load_diabetes\n",
+    "diabetes=load_diabetes()"
    ]
   },
   {
@@ -53,11 +57,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 177,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['data', 'target', 'frame', 'DESCR', 'feature_names', 'data_filename', 'target_filename', 'data_module'])"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "diabetes.keys()"
    ]
   },
   {
@@ -73,13 +89,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 178,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
-   "source": [
-    "# Your code here:\n"
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ".. _diabetes_dataset:\n",
+      "\n",
+      "Diabetes dataset\n",
+      "----------------\n",
+      "\n",
+      "Ten baseline variables, age, sex, body mass index, average blood\n",
+      "pressure, and six blood serum measurements were obtained for each of n =\n",
+      "442 diabetes patients, as well as the response of interest, a\n",
+      "quantitative measure of disease progression one year after baseline.\n",
+      "\n",
+      "**Data Set Characteristics:**\n",
+      "\n",
+      "  :Number of Instances: 442\n",
+      "\n",
+      "  :Number of Attributes: First 10 columns are numeric predictive values\n",
+      "\n",
+      "  :Target: Column 11 is a quantitative measure of disease progression one year after baseline\n",
+      "\n",
+      "  :Attribute Information:\n",
+      "      - age     age in years\n",
+      "      - sex\n",
+      "      - bmi     body mass index\n",
+      "      - bp      average blood pressure\n",
+      "      - s1      tc, total serum cholesterol\n",
+      "      - s2      ldl, low-density lipoproteins\n",
+      "      - s3      hdl, high-density lipoproteins\n",
+      "      - s4      tch, total cholesterol / HDL\n",
+      "      - s5      ltg, possibly log of serum triglycerides level\n",
+      "      - s6      glu, blood sugar level\n",
+      "\n",
+      "Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times the square root of `n_samples` (i.e. the sum of squares of each column totals 1).\n",
+      "\n",
+      "Source URL:\n",
+      "https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\n",
+      "\n",
+      "For more information see:\n",
+      "Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\n",
+      "(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "print(diabetes['DESCR'])"
    ]
   },
   {
@@ -97,11 +160,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 179,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Enter your answer here:\n"
+    "# Enter your answer here:\n",
+    "#1-> 10, information about the people who took exams\n",
+    "#2-> diabetes['target'] is a parameter that calculates the progression of the disease after a year\n",
+    "#3-> 442"
    ]
   },
   {
@@ -115,11 +181,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 180,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442, 10)\n",
+      "(442,)\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "print(diabetes['data'].shape)\n",
+    "print(diabetes['target'].shape)"
    ]
   },
   {
@@ -156,11 +233,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 181,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.model_selection import train_test_split"
    ]
   },
   {
@@ -172,11 +251,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 182,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "diabetes_model=LinearRegression()"
    ]
   },
   {
@@ -190,11 +270,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 183,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "diabetes_data_train=diabetes['data'][:-20]\n",
+    "diabetes_data_test=diabetes['data'][-20:]\n",
+    "diabetes_target_train=diabetes['target'][:-20]\n",
+    "diabetes_target_test=diabetes['target'][-20:]\n",
+    "\n",
+    "# diabetes_data_train, diabetes_data_test, diabetes_target_train, diabetes_target_test = train_test_split(diabetes['data'], diabetes['target'], test_size= 20)"
    ]
   },
   {
@@ -206,11 +292,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 184,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intercept: 152.76429169049118\n",
+      "Coefficients: [ 3.06094248e-01 -2.37635570e+02  5.10538048e+02  3.27729878e+02\n",
+      " -8.14111926e+02  4.92799595e+02  1.02841240e+02  1.84603496e+02\n",
+      "  7.43509388e+02  7.60966464e+01]\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "diabetes_model.fit(diabetes_data_train,diabetes_target_train)\n",
+    "print(\"Intercept:\",diabetes_model.intercept_)\n",
+    "print(\"Coefficients:\", diabetes_model.coef_)"
    ]
   },
   {
@@ -231,11 +331,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n"
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([197.61898486, 155.44031962, 172.88875144, 111.53270645,\n",
+       "       164.79397301, 131.06765869, 259.12441219, 100.47873746,\n",
+       "       117.06005372, 124.30261597, 218.36868146,  61.19581944,\n",
+       "       132.24837933, 120.33293546,  52.54513009, 194.03746764,\n",
+       "       102.5756431 , 123.56778709, 211.03465323,  52.60221696])"
+      ]
+     },
+     "execution_count": 185,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "diabetes_model.predict(diabetes_data_test)"
    ]
   },
   {
@@ -247,11 +363,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 186,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([233.,  91., 111., 152., 120.,  67., 310.,  94., 183.,  66., 173.,\n",
+       "        72.,  49.,  64.,  48., 178., 104., 132., 220.,  57.])"
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "diabetes_target_test"
    ]
   },
   {
@@ -263,11 +392,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 187,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your explanation here:\n"
+    "# Your explanation here:\n",
+    "#No we need to train the model better"
    ]
   },
   {
@@ -302,11 +432,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n"
+   "execution_count": 188,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                      y   R-squared:                       0.512\n",
+      "Model:                            OLS   Adj. R-squared:                  0.500\n",
+      "Method:                 Least Squares   F-statistic:                     43.16\n",
+      "Date:                Thu, 24 Aug 2023   Prob (F-statistic):           4.65e-58\n",
+      "Time:                        15:54:15   Log-Likelihood:                -2281.1\n",
+      "No. Observations:                 422   AIC:                             4584.\n",
+      "Df Residuals:                     411   BIC:                             4629.\n",
+      "Df Model:                          10                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const        152.7643      2.658     57.468      0.000     147.539     157.990\n",
+      "x1             0.3061     61.286      0.005      0.996    -120.167     120.779\n",
+      "x2          -237.6356     62.837     -3.782      0.000    -361.158    -114.113\n",
+      "x3           510.5380     68.156      7.491      0.000     376.561     644.515\n",
+      "x4           327.7299     66.876      4.901      0.000     196.267     459.192\n",
+      "x5          -814.1119    424.040     -1.920      0.056   -1647.669      19.445\n",
+      "x6           492.7996    344.223      1.432      0.153    -183.857    1169.457\n",
+      "x7           102.8412    219.462      0.469      0.640    -328.566     534.248\n",
+      "x8           184.6035    167.336      1.103      0.271    -144.338     513.545\n",
+      "x9           743.5094    175.357      4.240      0.000     398.801    1088.218\n",
+      "x10           76.0966     68.293      1.114      0.266     -58.151     210.344\n",
+      "==============================================================================\n",
+      "Omnibus:                        1.544   Durbin-Watson:                   2.026\n",
+      "Prob(Omnibus):                  0.462   Jarque-Bera (JB):                1.421\n",
+      "Skew:                           0.004   Prob(JB):                        0.491\n",
+      "Kurtosis:                       2.716   Cond. No.                         224.\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "import statsmodels.api as sm\n",
+    "\n",
+    "diabetes_data_train_const = sm.add_constant(diabetes_data_train)\n",
+    "\n",
+    "mod = sm.OLS( diabetes_target_train, diabetes_data_train_const)\n",
+    "\n",
+    "res = mod.fit()\n",
+    "\n",
+    "print(res.summary())"
    ]
   },
   {
@@ -326,7 +506,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 189,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -351,11 +531,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 190,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "auto=pd.read_csv('../auto-mpg.csv')"
    ]
   },
   {
@@ -367,11 +548,124 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n"
+   "execution_count": 191,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horse_power</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model_year</th>\n",
+       "      <th>car_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>3504</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"chevrolet chevelle malibu\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>3693</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"buick skylark 320\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3436</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"plymouth satellite\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3433</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"amc rebel sst\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>302.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>3449</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"ford torino\"</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mpg  cylinders  displacement  horse_power  weight  acceleration  \\\n",
+       "0  18.0          8         307.0        130.0    3504          12.0   \n",
+       "1  15.0          8         350.0        165.0    3693          11.5   \n",
+       "2  18.0          8         318.0        150.0    3436          11.0   \n",
+       "3  16.0          8         304.0        150.0    3433          12.0   \n",
+       "4  17.0          8         302.0        140.0    3449          10.5   \n",
+       "\n",
+       "   model_year                       car_name  \n",
+       "0          70  \\t\"chevrolet chevelle malibu\"  \n",
+       "1          70          \\t\"buick skylark 320\"  \n",
+       "2          70         \\t\"plymouth satellite\"  \n",
+       "3          70              \\t\"amc rebel sst\"  \n",
+       "4          70                \\t\"ford torino\"  "
+      ]
+     },
+     "execution_count": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "auto.head()"
    ]
   },
   {
@@ -383,11 +677,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n"
+   "execution_count": 192,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 398 entries, 0 to 397\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column        Non-Null Count  Dtype  \n",
+      "---  ------        --------------  -----  \n",
+      " 0   mpg           398 non-null    float64\n",
+      " 1   cylinders     398 non-null    int64  \n",
+      " 2   displacement  398 non-null    float64\n",
+      " 3   horse_power   392 non-null    float64\n",
+      " 4   weight        398 non-null    int64  \n",
+      " 5   acceleration  398 non-null    float64\n",
+      " 6   model_year    398 non-null    int64  \n",
+      " 7   car_name      398 non-null    object \n",
+      "dtypes: float64(4), int64(3), object(1)\n",
+      "memory usage: 25.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "auto.info()"
    ]
   },
   {
@@ -399,11 +716,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 193,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "70"
+      ]
+     },
+     "execution_count": 193,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "auto['model_year'].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The oldest model year is 70\n",
+      "The newest model year is 82\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "print(f'''The oldest model year is {auto['model_year'].min()}\n",
+    "The newest model year is {auto['model_year'].max()}''')"
    ]
   },
   {
@@ -415,11 +763,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n"
+   "execution_count": 195,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "mpg             0\n",
+       "cylinders       0\n",
+       "displacement    0\n",
+       "horse_power     0\n",
+       "weight          0\n",
+       "acceleration    0\n",
+       "model_year      0\n",
+       "car_name        0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 195,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "auto.dropna(inplace=True)\n",
+    "auto.isna().sum()\n"
    ]
   },
   {
@@ -431,11 +800,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n"
+   "execution_count": 196,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "cylinders\n",
+       "4    199\n",
+       "8    103\n",
+       "6     83\n",
+       "3      4\n",
+       "5      3\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 196,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "auto['cylinders'].value_counts()\n",
+    "#there are 5 cylinders values possible"
    ]
   },
   {
@@ -451,11 +839,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 197,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "auto.drop('car_name',axis=1,inplace=True)\n",
+    "feature=auto.drop('mpg',axis=1)\n",
+    "target=auto['mpg']\n",
+    "X_train,X_test,y_train,y_test=train_test_split(feature,target,test_size=0.2)\n"
    ]
   },
   {
@@ -469,11 +861,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n"
+   "execution_count": 216,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 216,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "auto_model=LinearRegression()\n",
+    "auto_model.fit(X_train,y_train)\n"
    ]
   },
   {
@@ -493,11 +901,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 220,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.815045703503711"
+      ]
+     },
+     "execution_count": 220,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "from sklearn.metrics import r2_score\n",
+    "y_pred=auto_model.predict(X_train)\n",
+    "r2_score(y_train,y_pred)"
    ]
   },
   {
@@ -513,11 +935,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 221,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7754354118636195"
+      ]
+     },
+     "execution_count": 221,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "y_test_pred = model.predict(X_test)\n",
+    "r2_score(y_test,y_test_pred)"
    ]
   },
   {
@@ -542,11 +977,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 222,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "X_train09,X_test09,y_train09,y_test09=train_test_split(feature,target,test_size=0.1)"
    ]
   },
   {
@@ -558,11 +994,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n"
+   "execution_count": 223,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 223,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here:\n",
+    "auto_model09=LinearRegression()\n",
+    "auto_model09.fit(X_train09,y_train09)"
    ]
   },
   {
@@ -574,11 +1026,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 226,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8080536802305803"
+      ]
+     },
+     "execution_count": 226,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "y_pred09=auto_model.predict(X_train09)\n",
+    "r2_score(y_train09,y_pred09)"
    ]
   },
   {
@@ -590,11 +1055,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 227,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8135694691090268"
+      ]
+     },
+     "execution_count": 227,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n"
+    "# Your code here:\n",
+    "y_test_pred09 = model.predict(X_test09)\n",
+    "r2_score(y_test09,y_test_pred09)"
    ]
   },
   {
@@ -610,7 +1088,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 205,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -626,7 +1104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 206,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -642,7 +1120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 207,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -660,7 +1138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 208,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -676,7 +1154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 209,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -717,7 +1195,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,