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--- 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 +}