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ML_Models/Decision_Tree.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GP7jOLzjHy45"
+   },
+   "source": [
+    "## Decision Tree as Regressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "CBseI4jKG6ik"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "lif0Z2npHQFR"
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"dataset/dev.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "id": "BdWQcpdHHeun"
+   },
+   "outputs": [],
+   "source": [
+    "input_data = df.iloc[:, :-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mLG2ZCtvHhYE",
+    "outputId": "fee83798-7f82-42ca-d46a-c609ce64fd96"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3964, 1)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target_col = np.array(df[' shares']).reshape(-1, 1)\n",
+    "target_col.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "id": "6gJ0jUOpHjYx"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "x_train, x_test, y_train, y_test = train_test_split(input_data, target_col, test_size = 0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "id": "dFbBqhSxHmF5"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "dtr = DecisionTreeRegressor()\n",
+    "dtr.fit(x_train, y_train)\n",
+    "y_pred = dtr.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "9uUWzuvdHoSx",
+    "outputId": "fdcf783c-5cc8-4df4-9230-4d4156588ecf"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "84333.98655462185"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error\n",
+    "mse_loss = mean_squared_error(y_pred, y_test)\n",
+    "mse_loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "id": "l1kIsm_ZHqrm"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lf0MaOw6HtTf"
+   },
+   "source": [
+    "## Decision Tree as Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "id": "tPhs7auDGtu-"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "iris = load_iris()\n",
+    "x = iris.data\n",
+    "y = iris.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "id": "dDnzl0s3Gy9f"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "dtr_classifier = DecisionTreeClassifier(max_depth = 1000, random_state = 0)\n",
+    "dtr_classifier = dtr_classifier.fit(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
+    },
+    "id": "pOUtmvqiG1YR",
+    "outputId": "19ae91a2-9c13-48b6-bf37-19b311eab9fc"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn import tree\n",
+    "from matplotlib import pyplot as plt\n",
+    "tree.plot_tree(dtr_classifier)\n",
+    "plt.show(dtr_classifier)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "id": "xZKF5YHaG367"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "Decision_Tree.ipynb",
+   "provenance": []
+  },
+  "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}