OSS Assignment 8.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 0)\t1\n",
      "  (1, 1)\t1\n",
      "  (2, 2)\t1\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.sparse import csr_matrix\n",
    "\n",
    "list1=[\n",
    "    [1,0,0],\n",
    "    [0,1,0],\n",
    "    [0,0,1]\n",
    "]\n",
    "\n",
    "res= np.array(list1)\n",
    "print(csr_matrix(res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       sepallength  sepalwidth  petallength  petalwidth\n",
      "count   150.000000  150.000000   150.000000  150.000000\n",
      "mean      5.843333    3.054000     3.758667    1.198667\n",
      "std       0.828066    0.433594     1.764420    0.763161\n",
      "min       4.300000    2.000000     1.000000    0.100000\n",
      "25%       5.100000    2.800000     1.600000    0.300000\n",
      "50%       5.800000    3.000000     4.350000    1.300000\n",
      "75%       6.400000    3.300000     5.100000    1.800000\n",
      "max       7.900000    4.400000     6.900000    2.500000\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('C:\\\\Users\\\\Rajesh kumar\\\\Downloads\\\\iris_csv.csv')\n",
    "print (data.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "70% train data:\n",
      "[[5.4 3.4 1.7 0.2]\n",
      " [5.6 3.  4.5 1.5]\n",
      " [6.2 3.4 5.4 2.3]\n",
      " [5.4 3.7 1.5 0.2]\n",
      " [5.  3.5 1.6 0.6]\n",
      " [6.4 3.2 5.3 2.3]\n",
      " [6.9 3.1 5.1 2.3]\n",
      " [6.8 2.8 4.8 1.4]\n",
      " [5.  3.6 1.4 0.2]\n",
      " [5.7 3.8 1.7 0.3]\n",
      " [6.8 3.2 5.9 2.3]\n",
      " [4.6 3.4 1.4 0.3]\n",
      " [6.4 2.8 5.6 2.1]\n",
      " [6.2 2.2 4.5 1.5]\n",
      " [5.9 3.  5.1 1.8]\n",
      " [7.7 2.6 6.9 2.3]\n",
      " [7.2 3.2 6.  1.8]\n",
      " [5.  2.  3.5 1. ]\n",
      " [5.1 3.4 1.5 0.2]\n",
      " [5.4 3.9 1.7 0.4]\n",
      " [6.7 3.1 4.7 1.5]\n",
      " [6.8 3.  5.5 2.1]\n",
      " [4.4 2.9 1.4 0.2]\n",
      " [5.6 2.9 3.6 1.3]\n",
      " [6.  3.  4.8 1.8]\n",
      " [5.  3.4 1.6 0.4]\n",
      " [4.9 2.5 4.5 1.7]\n",
      " [6.9 3.1 4.9 1.5]\n",
      " [5.  3.5 1.3 0.3]\n",
      " [4.4 3.2 1.3 0.2]\n",
      " [4.3 3.  1.1 0.1]\n",
      " [5.4 3.  4.5 1.5]\n",
      " [5.1 3.8 1.6 0.2]\n",
      " [6.5 3.2 5.1 2. ]\n",
      " [7.2 3.6 6.1 2.5]\n",
      " [6.1 3.  4.6 1.4]\n",
      " [5.1 3.8 1.9 0.4]\n",
      " [6.3 3.3 6.  2.5]\n",
      " [6.  2.7 5.1 1.6]\n",
      " [7.6 3.  6.6 2.1]\n",
      " [6.7 3.3 5.7 2.1]\n",
      " [6.3 2.9 5.6 1.8]\n",
      " [4.4 3.  1.3 0.2]\n",
      " [5.8 2.8 5.1 2.4]\n",
      " [5.  3.2 1.2 0.2]\n",
      " [5.5 3.5 1.3 0.2]\n",
      " [6.3 2.8 5.1 1.5]\n",
      " [5.6 2.8 4.9 2. ]\n",
      " [4.8 3.  1.4 0.3]\n",
      " [7.9 3.8 6.4 2. ]\n",
      " [5.8 2.7 3.9 1.2]\n",
      " [6.4 2.7 5.3 1.9]\n",
      " [5.1 2.5 3.  1.1]\n",
      " [6.6 3.  4.4 1.4]\n",
      " [5.8 2.6 4.  1.2]\n",
      " [4.8 3.  1.4 0.1]\n",
      " [5.6 3.  4.1 1.3]\n",
      " [5.2 2.7 3.9 1.4]\n",
      " [4.6 3.1 1.5 0.2]\n",
      " [5.  3.4 1.5 0.2]\n",
      " [6.3 2.5 4.9 1.5]\n",
      " [6.3 2.7 4.9 1.8]\n",
      " [4.9 3.1 1.5 0.1]\n",
      " [4.9 3.1 1.5 0.1]\n",
      " [4.6 3.2 1.4 0.2]\n",
      " [5.5 2.5 4.  1.3]\n",
      " [6.2 2.9 4.3 1.3]\n",
      " [5.4 3.9 1.3 0.4]\n",
      " [6.5 2.8 4.6 1.5]\n",
      " [6.  2.2 5.  1.5]\n",
      " [5.  3.3 1.4 0.2]\n",
      " [7.7 3.8 6.7 2.2]\n",
      " [6.9 3.2 5.7 2.3]\n",
      " [7.1 3.  5.9 2.1]\n",
      " [5.7 3.  4.2 1.2]\n",
      " [7.3 2.9 6.3 1.8]\n",
      " [5.5 2.4 3.7 1. ]\n",
      " [6.3 3.3 4.7 1.6]\n",
      " [5.7 2.8 4.1 1.3]\n",
      " [6.4 2.8 5.6 2.2]\n",
      " [6.4 3.2 4.5 1.5]\n",
      " [5.5 2.4 3.8 1.1]\n",
      " [5.  3.  1.6 0.2]\n",
      " [5.1 3.8 1.5 0.3]\n",
      " [6.1 2.9 4.7 1.4]\n",
      " [5.7 4.4 1.5 0.4]\n",
      " [5.8 2.7 5.1 1.9]\n",
      " [6.7 2.5 5.8 1.8]\n",
      " [6.5 3.  5.8 2.2]\n",
      " [6.  2.9 4.5 1.5]\n",
      " [6.3 2.3 4.4 1.3]\n",
      " [6.  3.4 4.5 1.6]\n",
      " [7.7 3.  6.1 2.3]\n",
      " [7.2 3.  5.8 1.6]\n",
      " [5.8 2.7 5.1 1.9]\n",
      " [5.1 3.3 1.7 0.5]\n",
      " [5.6 2.5 3.9 1.1]\n",
      " [5.9 3.  4.2 1.5]\n",
      " [5.9 3.2 4.8 1.8]\n",
      " [5.7 2.6 3.5 1. ]\n",
      " [6.4 3.1 5.5 1.8]\n",
      " [6.3 2.5 5.  1.9]\n",
      " [7.4 2.8 6.1 1.9]\n",
      " [5.2 4.1 1.5 0.1]\n",
      " [6.9 3.1 5.4 2.1]]\n",
      "['Iris-setosa' 'Iris-versicolor' 'Iris-virginica' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-virginica' 'Iris-virginica' 'Iris-versicolor'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-virginica' 'Iris-setosa'\n",
      " 'Iris-virginica' 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-versicolor' 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor'\n",
      " 'Iris-virginica' 'Iris-setosa' 'Iris-virginica' 'Iris-versicolor'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor' 'Iris-setosa'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-versicolor' 'Iris-setosa'\n",
      " 'Iris-virginica' 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-setosa' 'Iris-virginica' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-virginica' 'Iris-virginica' 'Iris-setosa'\n",
      " 'Iris-virginica' 'Iris-versicolor' 'Iris-virginica' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor'\n",
      " 'Iris-virginica' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa' 'Iris-versicolor'\n",
      " 'Iris-virginica' 'Iris-setosa' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-versicolor' 'Iris-virginica' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor'\n",
      " 'Iris-setosa' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica'\n",
      " 'Iris-virginica' 'Iris-virginica' 'Iris-setosa' 'Iris-virginica']\n",
      "\n",
      "30% test data:\n",
      "[[5.5 2.3 4.  1.3]\n",
      " [4.8 3.1 1.6 0.2]\n",
      " [6.7 3.1 4.4 1.4]\n",
      " [7.7 2.8 6.7 2. ]\n",
      " [4.8 3.4 1.9 0.2]\n",
      " [6.1 2.8 4.  1.3]\n",
      " [6.7 3.1 5.6 2.4]\n",
      " [4.5 2.3 1.3 0.3]\n",
      " [5.8 4.  1.2 0.2]\n",
      " [5.2 3.4 1.4 0.2]\n",
      " [5.8 2.7 4.1 1. ]\n",
      " [4.6 3.6 1.  0.2]\n",
      " [6.1 2.6 5.6 1.4]\n",
      " [4.9 3.  1.4 0.2]\n",
      " [5.6 2.7 4.2 1.3]\n",
      " [5.4 3.4 1.5 0.4]\n",
      " [5.7 2.8 4.5 1.3]\n",
      " [6.7 3.  5.  1.7]\n",
      " [5.2 3.5 1.5 0.2]\n",
      " [4.9 3.1 1.5 0.1]\n",
      " [6.3 3.4 5.6 2.4]\n",
      " [4.7 3.2 1.6 0.2]\n",
      " [6.7 3.  5.2 2.3]\n",
      " [5.5 2.6 4.4 1.2]\n",
      " [5.  2.3 3.3 1. ]\n",
      " [5.1 3.5 1.4 0.2]\n",
      " [6.6 2.9 4.6 1.3]\n",
      " [6.7 3.3 5.7 2.5]\n",
      " [6.4 2.9 4.3 1.3]\n",
      " [5.7 2.5 5.  2. ]\n",
      " [7.  3.2 4.7 1.4]\n",
      " [5.5 4.2 1.4 0.2]\n",
      " [5.3 3.7 1.5 0.2]\n",
      " [5.7 2.9 4.2 1.3]\n",
      " [6.  2.2 4.  1. ]\n",
      " [6.5 3.  5.5 1.8]\n",
      " [5.1 3.5 1.4 0.3]\n",
      " [6.2 2.8 4.8 1.8]\n",
      " [5.1 3.7 1.5 0.4]\n",
      " [4.8 3.4 1.6 0.2]\n",
      " [6.1 3.  4.9 1.8]\n",
      " [4.9 2.4 3.3 1. ]\n",
      " [4.7 3.2 1.3 0.2]\n",
      " [6.5 3.  5.2 2. ]\n",
      " [6.1 2.8 4.7 1.2]]\n",
      "['Iris-versicolor' 'Iris-setosa' 'Iris-versicolor' 'Iris-virginica'\n",
      " 'Iris-setosa' 'Iris-versicolor' 'Iris-virginica' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor' 'Iris-setosa'\n",
      " 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor' 'Iris-setosa'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-virginica' 'Iris-setosa' 'Iris-virginica' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-setosa' 'Iris-versicolor' 'Iris-virginica'\n",
      " 'Iris-versicolor' 'Iris-virginica' 'Iris-versicolor' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica'\n",
      " 'Iris-setosa' 'Iris-virginica' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-virginica' 'Iris-versicolor' 'Iris-setosa' 'Iris-virginica'\n",
      " 'Iris-versicolor']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "iris = pd.read_csv(\"C:\\\\Users\\\\Rajesh kumar\\\\Downloads\\\\iris_csv.csv\")\n",
    "X = iris.iloc[:, :-1].values\n",
    "y = iris.iloc[:, 4].values\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)\n",
    "print(\"\\n70% train data:\")\n",
    "print(X_train)\n",
    "print(y_train)\n",
    "print(\"\\n30% test data:\")\n",
    "print(X_test)\n",
    "print(y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response for test dataset:\n",
      "['Iris-virginica' 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa'\n",
      " 'Iris-virginica' 'Iris-setosa' 'Iris-virginica' 'Iris-versicolor'\n",
      " 'Iris-setosa' 'Iris-virginica' 'Iris-virginica' 'Iris-versicolor'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica' 'Iris-setosa'\n",
      " 'Iris-virginica' 'Iris-versicolor' 'Iris-virginica' 'Iris-setosa'\n",
      " 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa'\n",
      " 'Iris-setosa']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "iris = pd.read_csv(\"C:\\\\Users\\\\Rajesh kumar\\\\Downloads\\\\iris_csv.csv\")\n",
    "\n",
    "X = iris.iloc[:, :-1].values\n",
    "y = iris.iloc[:, 4].values\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
    "\n",
    "#Create KNN Classifier\n",
    "#Number of neighbors to use by default for kneighbors queries.\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "\n",
    "#Train the model using the training sets\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "print(\"Response for test dataset:\")\n",
    "y_pred = knn.predict(X_test)\n",
    "print(y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xtrain shape :  (404, 13)\n",
      "xtest shape  :  (102, 13)\n",
      "ytrain shape :  (404,)\n",
      "ytest shape  :  (102,)\n"
     ]
    },
    {
     "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": [
    "# Importing Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "   \n",
    "# Importing Data\n",
    "from sklearn.datasets import load_boston\n",
    "boston = load_boston()\n",
    "\n",
    "# Input Data\n",
    "x = boston.data\n",
    "   \n",
    "# Output Data\n",
    "y = boston.target\n",
    "   \n",
    "   \n",
    "# splitting data to training and testing dataset. \n",
    "from sklearn.model_selection import train_test_split\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size =0.2,\n",
    "                                                    random_state = 0)\n",
    "   \n",
    "print(\"xtrain shape : \", xtrain.shape)\n",
    "print(\"xtest shape  : \", xtest.shape)\n",
    "print(\"ytrain shape : \", ytrain.shape)\n",
    "print(\"ytest shape  : \", ytest.shape)\n",
    "\n",
    "# Fitting Multi Linear regression model to training model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(xtrain, ytrain)\n",
    "   \n",
    "# predicting the test set results\n",
    "y_pred = regressor.predict(xtest)\n",
    "\n",
    "plt.scatter(ytest, y_pred, c = 'green')\n",
    "plt.xlabel(\"Price: in $1000's\")\n",
    "plt.ylabel(\"Predicted value\")\n",
    "plt.title(\"True value vs predicted value : Linear Regression\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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