--- a
+++ b/Logistic regression model.ipynb
@@ -0,0 +1,517 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "e7fc773c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import scipy.stats as stats\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "from sklearn import linear_model\n",
+    "from sklearn import preprocessing\n",
+    "df=pd.read_csv('heart_data.csv')\n",
+    "\n",
+    "#x_list=['BMI','PhysicalHealth','SleepTime']\n",
+    "#x_data=df[x_list]\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "aa8974a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "smoke_new=preprocessing.LabelEncoder()\n",
+    "smoke_new=smoke_new.fit_transform(df['Smoking'])\n",
+    "df['Smoking']=smoke_new"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "f0f1b529",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns=['HeartDisease','AlcoholDrinking','Stroke','DiffWalking','Diabetic','Sex','Diabetic','PhysicalActivity','Asthma','KidneyDisease','SkinCancer','Race','GenHealth','AgeCategory']\n",
+    "for column in columns:\n",
+    "    temp=preprocessing.LabelEncoder()\n",
+    "    df[column]=temp.fit_transform(df[column])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "e4326dcd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_column='HeartDisease'\n",
+    "feature_column=[x for x in df.columns if x != y_column]\n",
+    "x_data=df[feature_column]\n",
+    "y_data=df['HeartDisease']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "28aac296",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    292422\n",
+       "1     27373\n",
+       "Name: HeartDisease, dtype: int64"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['HeartDisease'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "65cea96c",
+   "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>BMI</th>\n",
+       "      <th>Smoking</th>\n",
+       "      <th>AlcoholDrinking</th>\n",
+       "      <th>Stroke</th>\n",
+       "      <th>PhysicalHealth</th>\n",
+       "      <th>MentalHealth</th>\n",
+       "      <th>DiffWalking</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>AgeCategory</th>\n",
+       "      <th>Race</th>\n",
+       "      <th>Diabetic</th>\n",
+       "      <th>PhysicalActivity</th>\n",
+       "      <th>GenHealth</th>\n",
+       "      <th>SleepTime</th>\n",
+       "      <th>Asthma</th>\n",
+       "      <th>KidneyDisease</th>\n",
+       "      <th>SkinCancer</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.844750</td>\n",
+       "      <td>1.193474</td>\n",
+       "      <td>-0.27032</td>\n",
+       "      <td>-0.198040</td>\n",
+       "      <td>-0.046751</td>\n",
+       "      <td>3.281069</td>\n",
+       "      <td>-0.401578</td>\n",
+       "      <td>-0.951711</td>\n",
+       "      <td>0.136184</td>\n",
+       "      <td>0.497653</td>\n",
+       "      <td>2.372175</td>\n",
+       "      <td>0.538256</td>\n",
+       "      <td>1.159288</td>\n",
+       "      <td>-1.460354</td>\n",
+       "      <td>2.541515</td>\n",
+       "      <td>-0.195554</td>\n",
+       "      <td>3.118419</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.256338</td>\n",
+       "      <td>-0.837890</td>\n",
+       "      <td>-0.27032</td>\n",
+       "      <td>5.049478</td>\n",
+       "      <td>-0.424070</td>\n",
+       "      <td>-0.490039</td>\n",
+       "      <td>-0.401578</td>\n",
+       "      <td>-0.951711</td>\n",
+       "      <td>1.538806</td>\n",
+       "      <td>0.497653</td>\n",
+       "      <td>-0.419253</td>\n",
+       "      <td>0.538256</td>\n",
+       "      <td>1.159288</td>\n",
+       "      <td>-0.067601</td>\n",
+       "      <td>-0.393466</td>\n",
+       "      <td>-0.195554</td>\n",
+       "      <td>-0.320675</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.274603</td>\n",
+       "      <td>1.193474</td>\n",
+       "      <td>-0.27032</td>\n",
+       "      <td>-0.198040</td>\n",
+       "      <td>2.091388</td>\n",
+       "      <td>3.281069</td>\n",
+       "      <td>-0.401578</td>\n",
+       "      <td>1.050739</td>\n",
+       "      <td>0.697233</td>\n",
+       "      <td>0.497653</td>\n",
+       "      <td>2.372175</td>\n",
+       "      <td>0.538256</td>\n",
+       "      <td>-0.795561</td>\n",
+       "      <td>0.628776</td>\n",
+       "      <td>2.541515</td>\n",
+       "      <td>-0.195554</td>\n",
+       "      <td>-0.320675</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.647473</td>\n",
+       "      <td>-0.837890</td>\n",
+       "      <td>-0.27032</td>\n",
+       "      <td>-0.198040</td>\n",
+       "      <td>-0.424070</td>\n",
+       "      <td>-0.490039</td>\n",
+       "      <td>-0.401578</td>\n",
+       "      <td>-0.951711</td>\n",
+       "      <td>1.258282</td>\n",
+       "      <td>0.497653</td>\n",
+       "      <td>-0.419253</td>\n",
+       "      <td>-1.857852</td>\n",
+       "      <td>-0.143945</td>\n",
+       "      <td>-0.763977</td>\n",
+       "      <td>-0.393466</td>\n",
+       "      <td>-0.195554</td>\n",
+       "      <td>3.118419</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.726138</td>\n",
+       "      <td>-0.837890</td>\n",
+       "      <td>-0.27032</td>\n",
+       "      <td>-0.198040</td>\n",
+       "      <td>3.097572</td>\n",
+       "      <td>-0.490039</td>\n",
+       "      <td>2.490174</td>\n",
+       "      <td>-0.951711</td>\n",
+       "      <td>-0.705388</td>\n",
+       "      <td>0.497653</td>\n",
+       "      <td>-0.419253</td>\n",
+       "      <td>0.538256</td>\n",
+       "      <td>1.159288</td>\n",
+       "      <td>0.628776</td>\n",
+       "      <td>-0.393466</td>\n",
+       "      <td>-0.195554</td>\n",
+       "      <td>-0.320675</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        BMI   Smoking  AlcoholDrinking    Stroke  PhysicalHealth  \\\n",
+       "0 -1.844750  1.193474         -0.27032 -0.198040       -0.046751   \n",
+       "1 -1.256338 -0.837890         -0.27032  5.049478       -0.424070   \n",
+       "2 -0.274603  1.193474         -0.27032 -0.198040        2.091388   \n",
+       "3 -0.647473 -0.837890         -0.27032 -0.198040       -0.424070   \n",
+       "4 -0.726138 -0.837890         -0.27032 -0.198040        3.097572   \n",
+       "\n",
+       "   MentalHealth  DiffWalking       Sex  AgeCategory      Race  Diabetic  \\\n",
+       "0      3.281069    -0.401578 -0.951711     0.136184  0.497653  2.372175   \n",
+       "1     -0.490039    -0.401578 -0.951711     1.538806  0.497653 -0.419253   \n",
+       "2      3.281069    -0.401578  1.050739     0.697233  0.497653  2.372175   \n",
+       "3     -0.490039    -0.401578 -0.951711     1.258282  0.497653 -0.419253   \n",
+       "4     -0.490039     2.490174 -0.951711    -0.705388  0.497653 -0.419253   \n",
+       "\n",
+       "   PhysicalActivity  GenHealth  SleepTime    Asthma  KidneyDisease  SkinCancer  \n",
+       "0          0.538256   1.159288  -1.460354  2.541515      -0.195554    3.118419  \n",
+       "1          0.538256   1.159288  -0.067601 -0.393466      -0.195554   -0.320675  \n",
+       "2          0.538256  -0.795561   0.628776  2.541515      -0.195554   -0.320675  \n",
+       "3         -1.857852  -0.143945  -0.763977 -0.393466      -0.195554    3.118419  \n",
+       "4          0.538256   1.159288   0.628776 -0.393466      -0.195554   -0.320675  "
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scalar=StandardScaler()\n",
+    "x=scalar.fit_transform(x_data)\n",
+    "x=pd.DataFrame(x,columns=feature_column)\n",
+    "x.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "4f0d452c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train,x_test,y_train,y_test=train_test_split(x,y_data,test_size=0.3,stratify=y_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "3e5694fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_data=preprocessing.LabelEncoder()\n",
+    "y_data=y_data.fit_transform(df['HeartDisease'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "ff77dc5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train,x_test,y_train,y_test=train_test_split(x_data,y_data,test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "86b76352",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "log_model=linear_model.LogisticRegression(solver='lbfgs')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "44ad81f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log_model.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "6582ca13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_predict=log_model.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "3fcee867",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import precision_score\n",
+    "from sklearn.metrics import recall_score\n",
+    "from sklearn.metrics import f1_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "bde935d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9533836543466945"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "precision_score(y_true=y_test,y_pred=y_predict)                                                                      +0.45"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "194dcf2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9909934821252222"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f1_score(y_true=y_test,y_pred=y_predict)                                                                             +0.8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "8dc3d23a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9778549664838513"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recall_score(y_true=y_test,y_pred=y_predict)                                                                     +0.86"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "e417a7c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.series.Series"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.iloc[0,:]\n",
+    "type(x.iloc[0,:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "3fbafc9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Expected 2D array, got 1D array instead:\narray=[16.6  1. ].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_1064\\4014638382.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m        'Smoking':1}\n\u001b[0;32m      3\u001b[0m \u001b[0mperson\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mperson\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mperson\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscalar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mperson\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[1;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[0;32m    850\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    851\u001b[0m             \u001b[1;31m# fit method of arity 1 (unsupervised transformation)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 852\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    853\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    854\u001b[0m             \u001b[1;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    804\u001b[0m         \u001b[1;31m# Reset internal state before fitting\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    805\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 806\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpartial_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    807\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    808\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpartial_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py\u001b[0m in \u001b[0;36mpartial_fit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    839\u001b[0m         \"\"\"\n\u001b[0;32m    840\u001b[0m         \u001b[0mfirst_call\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"n_samples_seen_\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 841\u001b[1;33m         X = self._validate_data(\n\u001b[0m\u001b[0;32m    842\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    843\u001b[0m             \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"csr\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"csc\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    564\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Validation should be done on X, y or both.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    565\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mno_val_y\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 566\u001b[1;33m             \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    567\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    568\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mno_val_y\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    767\u001b[0m             \u001b[1;31m# If input is 1D raise error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    768\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 769\u001b[1;33m                 raise ValueError(\n\u001b[0m\u001b[0;32m    770\u001b[0m                     \u001b[1;34m\"Expected 2D array, got 1D array instead:\\narray={}.\\n\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    771\u001b[0m                     \u001b[1;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[16.6  1. ].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
+     ]
+    }
+   ],
+   "source": [
+    "#person={'BMI':16.6,\n",
+    "#       'Smoking':1}\n",
+    "#person=pd.Series(person)\n",
+    "#person=scalar.fit_transform(person)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "432aed1e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}