diff --git "a/Insurance Analysis.ipynb" "b/Insurance Analysis.ipynb" new file mode 100644--- /dev/null +++ "b/Insurance Analysis.ipynb" @@ -0,0 +1,1286 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Descripition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Medical Insurance Cost Prediction\n", + "In this project, we analyze how personal attributes and lifestyle choices impact medical insurance costs. The dataset includes information about age, gender, BMI, family size, smoking habits, geographic location, and medical expenses. By studying these factors, we aim to identify key influences on insurance charges and build a predictive model to estimate future healthcare costs.\n", + "\n", + "Key Objectives:\n", + "\n", + "✔ Understand how different factors affect medical expenses.\n", + "\n", + "✔ Identify trends, such as how smoking or BMI influences insurance costs.\n", + "\n", + "✔ Build a machine learning model to predict insurance charges based on personal and geographic data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Data card\n", + "- https://www.kaggle.com/datasets/willianoliveiragibin/healthcare-insurance/data\n", + "### App\n", + "- https://huggingface.co/spaces/kanneboinakumar/Insurance-Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\"What
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.Requriment Package Loading " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "from sklearn.metrics import r2_score,root_mean_squared_error,mean_absolute_error\n", + "from joblib import dump,load" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.Understand the Dataset\n", + "- first check its structure, missing values, and key features" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'insurance.csv'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m insurance_df\u001b[38;5;241m=\u001b[39m\u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minsurance.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2\u001b[0m insurance_df\u001b[38;5;241m.\u001b[39mhead()\n", + "File \u001b[1;32mc:\\Users\\USER\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m 1014\u001b[0m dialect,\n\u001b[0;32m 1015\u001b[0m delimiter,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m 1023\u001b[0m )\n\u001b[0;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\USER\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m 619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m 622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m 623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", + "File \u001b[1;32mc:\\Users\\USER\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m 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\u001b[1;32mc:\\Users\\USER\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\io\\common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m 869\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m 870\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m 871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m 872\u001b[0m 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879\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 880\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 881\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m 882\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'insurance.csv'" + ] + } + ], + "source": [ + "insurance_df=pd.read_csv('insurance.csv')\n", + "insurance_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of insurance data : (1338, 7)\n" + ] + }, + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + " dtype nunique null_count\n", + "age int64 47 0\n", + "sex object 2 0\n", + "bmi float64 548 0\n", + "children int64 6 0\n", + "smoker object 2 0\n", + "region object 4 0\n", + "charges float64 1337 0" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(f\"shape of insurance data : {insurance_df.shape}\")\n", + "columns = insurance_df.columns\n", + "l=[]\n", + "for col in columns:\n", + " dtype = insurance_df[col].dtype\n", + " nunique = insurance_df[col].nunique()\n", + " null_count = insurance_df[col].isnull().sum()\n", + " l.append([dtype,nunique,null_count])\n", + "pd.DataFrame(l,columns=[\"dtype\",\"nunique\",\"null_count\"],index=columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.Preprocess the Data \n", + "- Handle missing values\n", + "- scale features\n", + "- encode categorical variables\n", + "- Drop unwanted columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1.Catgorial and Numerical Colums Superation" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Categorical columns: ['sex', 'smoker', 'region'] \n", + "Numerical columns: ['age', 'bmi', 'children', 'charges']\n" + ] + } + ], + "source": [ + "cat_cols = insurance_df.select_dtypes(include=['object']).columns.tolist()\n", + "num_cols = insurance_df.select_dtypes(include=['int64', 'float64']).columns.tolist()\n", + "print('Categorical columns:', cat_cols, '\\nNumerical columns:', num_cols)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2.Catgorial Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,5))\n", + "for i in range(len(cat_cols)):\n", + " count = insurance_df[cat_cols[i]].value_counts().keys()\n", + " plt.subplot(1,3, i + 1)\n", + " ax = sns.countplot(data=insurance_df,x=insurance_df[cat_cols[i]],color='lightgreen')\n", + " ax.bar_label(ax.containers[0],color='blue')\n", + " ax.set_title(f'Distribution plot {cat_cols[i]}')\n", + " ax.set_xlabel(cat_cols[i])\n", + " ax.set_ylabel('Count')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.3.Numarical Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for col in num_cols:\n", + " fig, axes = plt.subplots(1, 2, figsize=(8, 3))\n", + "\n", + " # Plot the distribution plot in the first subplot\n", + " sns.histplot(insurance_df[col], kde=True, ax=axes[0])\n", + " axes[0].set_title(f'{col} Distribution')\n", + "\n", + " # Plot the boxplot in the second subplot\n", + " sns.boxplot(x=insurance_df[col], ax=axes[1])\n", + " axes[1].set_title(f'{col} Boxplot')\n", + "\n", + " # Display the plots\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "# def remove_outliers_iqr(df, columns):\n", + "# for col in columns:\n", + "# Q1 = df[col].quantile(0.25)\n", + "# Q3 = df[col].quantile(0.75)\n", + "# IQR = Q3 - Q1\n", + "# lower_bound = Q1 - 1.5 * IQR\n", + "# upper_bound = Q3 + 1.5 * IQR\n", + "# df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)]\n", + "# return df\n", + "\n", + "# # Apply outlier removal\n", + "# insurance_df_cleaned = remove_outliers_iqr(insurance_df, num_cols)\n", + "\n", + "# # Check new shape after outlier removal\n", + "# print(\"Original Shape:\", insurance_df.shape)\n", + "# print(\"New Shape after Outlier Removal:\", insurance_df_cleaned.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countmeanstdmin25%50%75%max
age1338.039.20702514.04996018.000027.0000039.00051.00000064.00000
bmi1338.030.6633976.09818715.960026.2962530.40034.69375053.13000
children1338.01.0949181.2054930.00000.000001.0002.0000005.00000
charges1338.013270.42226512110.0112371121.87394740.287159382.03316639.91251563770.42801
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" + ], + "text/plain": [ + " count mean std min 25% 50% \\\n", + "age 1338.0 39.207025 14.049960 18.0000 27.00000 39.000 \n", + "bmi 1338.0 30.663397 6.098187 15.9600 26.29625 30.400 \n", + "children 1338.0 1.094918 1.205493 0.0000 0.00000 1.000 \n", + "charges 1338.0 13270.422265 12110.011237 1121.8739 4740.28715 9382.033 \n", + "\n", + " 75% max \n", + "age 51.000000 64.00000 \n", + "bmi 34.693750 53.13000 \n", + "children 2.000000 5.00000 \n", + "charges 16639.912515 63770.42801 " + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "insurance_df.describe().T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.4.LabelEncoder" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "encoder_d={}\n", + "for col in insurance_df[cat_cols]:\n", + " encoder=LabelEncoder()\n", + " insurance_df[col]=encoder.fit_transform(insurance_df[col])\n", + " encoder_d[col]=encoder" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.5.Save LabelEncoder" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['encoder_d.joblib']" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dump(encoder_d,'encoder_d.joblib')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.6.Correlation metrix" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.heatmap(insurance_df.corr(),annot=True,fmt=\".2f\")\n", + "plt.title(\"Correlation metrix\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.8.Train Test Split" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "X=insurance_df.drop(columns='charges')\n", + "y=insurance_df['charges']" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(268, 6)\n", + "(268,)\n", + "(1070, 6)\n", + "(1070,)\n" + ] + } + ], + "source": [ + "X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=42,test_size=0.2)\n", + "print(X_test.shape)\n", + "print(y_test.shape)\n", + "print(X_train.shape)\n", + "print(y_train.shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.Model Building" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
RandomForestRegressor()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "RandomForestRegressor()" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lr=LinearRegression()\n", + "lr.fit(X_train,y_train)\n", + "\n", + "dt=DecisionTreeRegressor()\n", + "dt.fit(X_train,y_train)\n", + "\n", + "Rf=RandomForestRegressor()\n", + "Rf.fit(X_train,y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.Evaluaction of Model" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train_Data_Report\n" + ] + }, + { + "data": { + "text/html": [ + "
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rmsemaer2adj_r2
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Rf1893.9229311041.8832510.9729680.972815
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" + ], + "text/plain": [ + " rmse mae r2 adj_r2\n", + "lr 6105.789320 4208.762029 0.651755 0.649789\n", + "dt 494.205984 29.572515 0.998305 0.998295\n", + "Rf 1893.922931 1041.883251 0.972968 0.972815" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def report(model,X,y):\n", + " predict=model.predict(X)\n", + " r2=r2_score(predict,y)\n", + " rmse=root_mean_squared_error(predict,y)\n", + " mae=mean_absolute_error(predict,y)\n", + " adj_r2=1 - ((1 - r2) * ( X.shape[0]- 1) / (X.shape[0] - X.shape[1] - 1))\n", + " return [rmse,mae,r2,adj_r2]\n", + "models=[lr,dt,Rf]\n", + "l=[]\n", + "for model in models:\n", + " l.append(report(model,X_train,y_train))\n", + "print('Train_Data_Report')\n", + "Train_Data_Report=pd.DataFrame(l,index=['lr','dt','Rf'],columns=['rmse','mae','r2','adj_r2'])\n", + "Train_Data_Report" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test_Data_Report\n" + ] + }, + { + "data": { + "text/html": [ + "
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r2rmsemaeadj_r2
lr5799.5870914186.5088980.7086170.701918
dt6713.1294583043.1874330.7540570.748403
Rf4635.5285832534.2850310.8534090.850039
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" + ], + "text/plain": [ + " r2 rmse mae adj_r2\n", + "lr 5799.587091 4186.508898 0.708617 0.701918\n", + "dt 6713.129458 3043.187433 0.754057 0.748403\n", + "Rf 4635.528583 2534.285031 0.853409 0.850039" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "l=[]\n", + "for model in models:\n", + " l.append(report(model,X_test,y_test))\n", + "print('Test_Data_Report')\n", + "Test_Data_Report=pd.DataFrame(l,index=['lr','dt','Rf'],columns=['r2','rmse','mae','adj_r2'])\n", + "Test_Data_Report" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.Save Best Model" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Rf_model.joblib']" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from joblib import dump\n", + "dump(Rf,'Rf_model.joblib')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.10.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}