Add files via upload

day57-elasticnet-regression/elastic-net-regression.ipynb

@@ -0,0 +1,166 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "71e28874",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_diabetes\n",
+    "from sklearn.linear_model import LinearRegression,Ridge,Lasso,ElasticNet\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import r2_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "284ccf3c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X,y = load_diabetes(return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "799d9080",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "8a242b9f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4399387660024645"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Linear Regression\n",
+    "reg = LinearRegression()\n",
+    "reg.fit(X_train,y_train)\n",
+    "y_pred = reg.predict(X_test)\n",
+    "r2_score(y_test,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "a41eb10c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4519973816947852"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Ridge \n",
+    "reg = Ridge(alpha=0.1)\n",
+    "reg.fit(X_train,y_train)\n",
+    "y_pred = reg.predict(X_test)\n",
+    "r2_score(y_test,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "b6b53e7c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4411227990495632"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Lasso\n",
+    "reg = Lasso(alpha=0.01)\n",
+    "reg.fit(X_train,y_train)\n",
+    "y_pred = reg.predict(X_test)\n",
+    "r2_score(y_test,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "5fc65b12",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4531493801165679"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ElasticNet\n",
+    "reg = ElasticNet(alpha=0.005,l1_ratio=0.9)\n",
+    "reg.fit(X_train,y_train)\n",
+    "y_pred = reg.predict(X_test)\n",
+    "r2_score(y_test,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0cb67b0c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}