6-Evolation-Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4dde6abc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"seaborn-whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d15b3ed0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random Forest with R_square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "74506f0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"csv/random+forest+regression+dataset.csv\",sep=\";\",header=None)\n",
    "\n",
    "x = df.iloc[:,0].values.reshape(-1,1)\n",
    "y = df.iloc[:,1].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7d6df0b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.5 seviyesinde fiyatın ne kadar olduğu :  [28.]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-bfdafa2ae484>:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  rf.fit(x,y)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf = RandomForestRegressor(n_estimators = 100, random_state = 42)\n",
    "rf.fit(x,y)\n",
    "print(\"7.5 seviyesinde fiyatın ne kadar olduğu : \",rf.predict([[7.5]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ce36a9d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_head = rf.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e6d78547",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r_score :  0.9798724794092587\n"
     ]
    }
   ],
   "source": [
    "# model degerlendirme metrics\n",
    "from sklearn.metrics import r2_score\n",
    "print(\"r_score : \", r2_score(y,y_head))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fdd26488",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lineer Regression with R_square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a9ea5b5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"csv/linear_regression_dataset.csv\", sep = \";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0318d827",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd011b099a0>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw linear regression with sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "linear_reg = LinearRegression()\n",
    "\n",
    "x = df.deneyim.values.reshape(-1,1)\n",
    "y = df.maas.values.reshape(-1,1)\n",
    "\n",
    "linear_reg.fit(x,y)\n",
    "\n",
    "y_head = linear_reg.predict(x)\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x, y_head, color=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fc9c22da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r_square score :  0.9775283164949902\n"
     ]
    }
   ],
   "source": [
    "print(\"r_square score : \", r2_score(y,y_head))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48e0930e",
   "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,
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