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Logistic regression model.ipynb
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{
"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": []
}
],
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"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",
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