HindiCodingZone.ipynb

hindicodingzone.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "HindiCodingZone.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/gist/sunnysinghnitb/a6911b9abdb3557cde7ddfe53f224c07/hindicodingzone.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "1auWNAj_HrcG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "outputId": "a8bdbc2b-e25d-404d-9890-34832055dcd5"
      },
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model\n",
        "import matplotlib.pyplot as plott\n",
        "\n",
        "features = [[120], [130], [140], [150], [160]]\n",
        "labels = [1120, 1180, 1280, 1320, 1515]\n",
        "plott.scatter(features, labels)\n",
        "plott.xlabel(\"DAYS\")\n",
        "plott.ylabel(\"PRICE\")\n",
        "cd = linear_model.LinearRegression()\n",
        "rd = cd.fit(features, labels)\n",
        "result = rd.predict([[110], [170]])\n",
        "plott.plot([[110], [170]], result)\n",
        "print(result)\n",
        "plott.show()\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1004. 1562.]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ist3atWuxa9cuyGQyzJ49GzExMUhPT4efnx+SkpJw/PhxJCUl4ZNPPrFXuURETkVb2YBN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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff6184effd0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "fVJsdXXq7wUh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "1f3589f0-0614-4bac-d7e7-1f2916cac50b"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris \n",
        "from sklearn import tree\n",
        "import numpy as no\n",
        "\n",
        "iris = load_iris()\n",
        "print(iris.keys())\n",
        "\n",
        "print(iris.feature_names)\n",
        "print(iris.target_names)\n",
        "print(iris.data[0])\n",
        "print(iris.target[0])\n",
        "remove = [0, 50, 100]\n",
        "new_target = no.delete(iris.target, remove)\n",
        "new_data = no.delete(iris.data, remove, axis = 0)\n",
        "\n",
        "clf = tree.DecisionTreeClassifier()\n",
        "clf = clf.fit(new_data, new_target)\n",
        "prediction = clf.predict(iris.data[remove])\n",
        "print(\"Original Labels: \", iris.target[remove])\n",
        "print(\"Predicted Labels: \", prediction)\n",
        "\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])\n",
            "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
            "['setosa' 'versicolor' 'virginica']\n",
            "[5.1 3.5 1.4 0.2]\n",
            "0\n",
            "Original Labels:  [0 1 2]\n",
            "Predicted Labels:  [0 1 2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ImZmr954SgW3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "outputId": "e5a8329e-d978-4422-c9b0-310aeb1e89a6"
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "x1 = np.array([4, 3, 4, 4, 8, 4])\n",
        "print(x1)\n",
        "x3 = x1[::-1]\n",
        "x2 = np.eye(3)\n",
        "print(x2)\n",
        "print(x3)\n",
        "\n",
        "import pandas as pd\n",
        "data = pd.DataFrame({'Country': ['Russia','Colombia','Chile','Equador','Nigeria'],\n",
        "                    'Rank':[121,40,100,130,11]})\n",
        "data\n",
        "print(data)\n",
        "data.describe()\n"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[4 3 4 4 8 4]\n",
            "[[1. 0. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 0. 1.]]\n",
            "[4 8 4 4 3 4]\n",
            "    Country  Rank\n",
            "0    Russia   121\n",
            "1  Colombia    40\n",
            "2     Chile   100\n",
            "3   Equador   130\n",
            "4   Nigeria    11\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>80.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>52.300096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>11.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>121.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>130.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Rank\n",
              "count    5.000000\n",
              "mean    80.400000\n",
              "std     52.300096\n",
              "min     11.000000\n",
              "25%     40.000000\n",
              "50%    100.000000\n",
              "75%    121.000000\n",
              "max    130.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    }
  ]
}