add module 3 week 3 exercise

Module_3/Week3/exercise.ipynb

@@ -0,0 +1,120 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "from sklearn.model_selection import train_test_split,GridSearchCV\n",
+    "from sklearn.metrics import accuracy_score,mean_squared_error\n",
+    "from sklearn.tree import DecisionTreeClassifier,DecisionTreeRegressor\n",
+    "from sklearn.datasets import fetch_openml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sample Test: [1 0 2 1 1 0 1 2 1 1 2 0 0 0 0 1 2 1 1 2 0 2 0 2 2 2 2 2 0 0]\n",
+      "Accuracy score 1.0 \n"
+     ]
+    }
+   ],
+   "source": [
+    "iris_X, iris_y = datasets.load_iris(return_X_y=True)\n",
+    "\n",
+    "X_train,X_test,y_train,y_test = train_test_split(iris_X,iris_y,test_size=0.2,random_state=42)\n",
+    "\n",
+    "dt_obj = DecisionTreeClassifier()\n",
+    "params = {'criterion':['gini','entropy'],\n",
+    "          'splitter':['best','random'],\n",
+    "          'max_depth':range(5,10,1)}\n",
+    "dt_classifier = GridSearchCV(dt_obj,params)\n",
+    "\n",
+    "dt_classifier.fit(X_train,y_train)\n",
+    "dt_classifier.best_params_\n",
+    "\n",
+    "y_pred = dt_classifier.predict(X_test)\n",
+    "print(\"Sample Test:\", y_pred)\n",
+    "print(f\"Accuracy score {accuracy_score ( y_test , y_pred )} \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/microwave/opt/anaconda3/lib/python3.9/site-packages/sklearn/datasets/_openml.py:320: UserWarning: Multiple active versions of the dataset matching the name machine_cpu exist. Versions may be fundamentally different, returning version 1. Available versions:\n",
+      "- version 1, status: active\n",
+      "  url: https://www.openml.org/search?type=data&id=230\n",
+      "- version 2, status: active\n",
+      "  url: https://www.openml.org/search?type=data&id=733\n",
+      "\n",
+      "  warn(warning_msg)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.03333333333333333"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "machine_cpu = fetch_openml(name='machine_cpu')\n",
+    "machine_data = machine_cpu.data\n",
+    "machine_labels = machine_cpu.target\n",
+    "\n",
+    "X_train,X_test,y_train,y_test = train_test_split(iris_X,iris_y,test_size=0.2,random_state=42)\n",
+    "\n",
+    "params = {'criterion':['poisson', 'squared_error', 'friedman_mse', 'absolute_error'],\n",
+    "          'splitter':['best','random'],\n",
+    "          'max_depth':range(5,10,1)}\n",
+    "dt_obj = DecisionTreeRegressor()\n",
+    "dt_regressor = GridSearchCV(dt_obj,params)\n",
+    "\n",
+    "dt_regressor.fit(X_train,y_train)\n",
+    "dt_regressor.best_params_\n",
+    "\n",
+    "y_pred = dt_regressor.predict(X_test)\n",
+    "mean_squared_error(y_test,y_pred)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}