Lassso_Regresyon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "030d7184",
   "metadata": {},
   "source": [
    "# Lassso Regresyon\n",
    "\n",
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d047eadd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.linear_model import Lasso, LassoCV\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4d11f7c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "hit = pd.read_csv(\"Hitters.csv\")\n",
    "df = hit.copy()\n",
    "df = df.dropna()\n",
    "\n",
    "dms = pd.get_dummies(df[[\"League\", \"Division\", \"NewLeague\"]])\n",
    "\n",
    "y = df[\"Salary\"]\n",
    "X_ = df.drop([\"Salary\", \"League\", \"Division\", \"NewLeague\"], axis=1).astype(\"float64\")\n",
    "X = pd.concat([X_, dms[[\"League_N\", \"Division_W\", \"NewLeague_N\"]]], axis = 1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=25,\n",
    "                                                   random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "49f983fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model\n",
    "lasso_model = Lasso(alpha=0.5, max_iter=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "afe27997",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso(alpha=0.5, max_iter=10000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(alpha=0.5, max_iter=10000)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Lasso(alpha=0.5, max_iter=10000)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modeli eğitme\n",
    "lasso_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "13380690",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.70017651e+00,  6.83109967e+00,  5.02628295e+00, -1.84332799e+00,\n",
       "       -1.29681669e+00,  5.04548951e+00,  8.82494552e-01, -2.17458320e-01,\n",
       "        7.30992711e-02, -8.31785548e-01,  1.59206105e+00,  1.15255444e+00,\n",
       "       -7.24191768e-01,  2.75315528e-01,  3.96130879e-01, -2.81588995e+00,\n",
       "        5.62751130e+01, -1.17779861e+02, -0.00000000e+00])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Katsayılara erişme \n",
    "lasso_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "112fd8b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.438e+04, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.039e+04, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+04, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.427e+03, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.150e+04, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.054e+04, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.350e+05, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.803e+06, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.727e+06, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.016e+06, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.833e+06, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.068e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.089e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.106e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.115e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.120e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.126e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.127e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.128e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.128e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.129e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.129e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.129e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.129e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.129e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.129e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.130e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.130e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.130e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.130e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.130e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.130e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.130e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.130e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.130e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/batuhanbilgili/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.130e+07, tolerance: 5.017e+03\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lasso = Lasso()\n",
    "\n",
    "# Farklı lambda değerlerinde model eğitimi ve katsayıları kaydetme\n",
    "lambdas = 10**np.linspace(10,-2,100)*0.5\n",
    "\n",
    "# Katsayıları tutacak dizi\n",
    "coef_array = []\n",
    "\n",
    "for i in lambdas:\n",
    "    lasso.set_params(alpha=i)\n",
    "    lasso.fit(X_train, y_train)\n",
    "    coef_array.append(lasso.coef_)\n",
    "\n",
    "# Katsayıları görselleştirme\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(lambdas, coef_array)\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Lambda')\n",
    "plt.ylabel('Katsayılar')\n",
    "plt.title('Lasso Katsayıları')\n",
    "plt.legend(X.columns, loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32b3dc1c",
   "metadata": {},
   "source": [
    "## Tahmin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "42c2635c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 597.52271834,  626.96511177,  905.05009957,  420.63560909,\n",
       "        439.74876227,  393.66348003,  597.059696  ,  434.49764893,\n",
       "        898.73667765,  782.27453586,  617.7696676 ,  918.4074515 ,\n",
       "        224.95457375,  413.43360928,  277.6745751 ,  449.85929924,\n",
       "        749.96224342,   86.20099487, 1195.99714366,  312.97060418,\n",
       "        436.85512698,  650.24537764,  547.69027691,  613.37810714,\n",
       "        587.64265074])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8e61d37a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lasso_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fcc888d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "263.4370756840094\n"
     ]
    }
   ],
   "source": [
    "print(np.sqrt(mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cc12d10",
   "metadata": {},
   "source": [
    "## Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "da284323",
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso_cv_model = LassoCV(alphas= None, \n",
    "                         cv = 10, \n",
    "                         max_iter=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0eb2010c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LassoCV(cv=10, max_iter=10000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LassoCV</label><div class=\"sk-toggleable__content\"><pre>LassoCV(cv=10, max_iter=10000)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LassoCV(cv=10, max_iter=10000)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_cv_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "89cb88d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.921147001831712"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_cv_model.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2f9deec6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "lasso_tuned = Lasso(alpha = lasso_cv_model.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3883f6a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso(alpha=1.921147001831712)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(alpha=1.921147001831712)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Lasso(alpha=1.921147001831712)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_tuned.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d4637f22",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lasso_tuned.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "faea7eb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "265.56448168227837"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_test, y_pred))"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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