Elastic_Net_Regresion.ipynb

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   "source": [
    "#i will compare side by side ridge lasso and elastic net regression\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_diabetes\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.linear_model import Ridge,Lasso,ElasticNet"
   ]
  },
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    "x,y=load_diabetes(return_X_y=True)"
   ]
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    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=42)"
   ]
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   "source": [
    "#Ridge regression\n",
    "l1=Ridge(alpha=0.001)\n",
    "l1.fit(x_train,y_train)\n",
    "y1=l1.predict(x_test)\n",
    "r2_score(y_test,y1)*100"
   ]
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    "#Lasso_regression\n",
    "l2=Lasso(alpha=0.1)\n",
    "l2.fit(x_train,y_train)\n",
    "y2=l2.predict(x_test)\n",
    "r2_score(y_test,y2)*100"
   ]
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   "source": [
    "#Elastic_Net_Regression\n",
    "l3=ElasticNet(alpha=0.005,l1_ratio=0.9)\n",
    "l3.fit(x_train,y_train)\n",
    "y3=l3.predict(x_test)\n",
    "r2_score(y_test,y3)"
   ]
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