Data-Mining/Mean-Square-Method.ipynb

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  "metadata": {
    "colab": {
      "name": "Mean-Square-Method.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nd6srH07S5Sd",
        "colab_type": "code",
        "outputId": "8c2db63a-512c-4735-b723-efcb7e5bf8b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/mntDrive') \n",
        "!ls \"/mntDrive/My Drive/Colab Notebooks\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /mntDrive\n",
            " ApacheSpark\t\t        Cosmetics.xlsx\n",
            " Apriori-Algorithm.ipynb        Introduction-To-Data-PreProcessing.ipynb\n",
            " Apriori-ARM-Assignment.ipynb   Mean-Square-Method.ipynb\n",
            "'car data.csv'\t\t        pima1.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UH8GD2NiUkg8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "23fex4mmcHkh",
        "colab_type": "code",
        "outputId": "de197ed6-5033-4043-98ab-97c8048053b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "df = pd.read_csv('/mntDrive/My Drive/Colab Notebooks/car data.csv')\n",
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "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>Car_Name</th>\n",
              "      <th>Year</th>\n",
              "      <th>Selling_Price</th>\n",
              "      <th>Present_Price</th>\n",
              "      <th>Kms_Driven</th>\n",
              "      <th>Fuel_Type</th>\n",
              "      <th>Seller_Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Owner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ritz</td>\n",
              "      <td>2014</td>\n",
              "      <td>3.35</td>\n",
              "      <td>5.59</td>\n",
              "      <td>27000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>sx4</td>\n",
              "      <td>2013</td>\n",
              "      <td>4.75</td>\n",
              "      <td>9.54</td>\n",
              "      <td>43000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ciaz</td>\n",
              "      <td>2017</td>\n",
              "      <td>7.25</td>\n",
              "      <td>9.85</td>\n",
              "      <td>6900</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>wagon r</td>\n",
              "      <td>2011</td>\n",
              "      <td>2.85</td>\n",
              "      <td>4.15</td>\n",
              "      <td>5200</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>swift</td>\n",
              "      <td>2014</td>\n",
              "      <td>4.60</td>\n",
              "      <td>6.87</td>\n",
              "      <td>42450</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>city</td>\n",
              "      <td>2016</td>\n",
              "      <td>9.50</td>\n",
              "      <td>11.60</td>\n",
              "      <td>33988</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>297</th>\n",
              "      <td>brio</td>\n",
              "      <td>2015</td>\n",
              "      <td>4.00</td>\n",
              "      <td>5.90</td>\n",
              "      <td>60000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>city</td>\n",
              "      <td>2009</td>\n",
              "      <td>3.35</td>\n",
              "      <td>11.00</td>\n",
              "      <td>87934</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>city</td>\n",
              "      <td>2017</td>\n",
              "      <td>11.50</td>\n",
              "      <td>12.50</td>\n",
              "      <td>9000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>brio</td>\n",
              "      <td>2016</td>\n",
              "      <td>5.30</td>\n",
              "      <td>5.90</td>\n",
              "      <td>5464</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>301 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Car_Name  Year  Selling_Price  ...  Seller_Type  Transmission Owner\n",
              "0       ritz  2014           3.35  ...       Dealer        Manual     0\n",
              "1        sx4  2013           4.75  ...       Dealer        Manual     0\n",
              "2       ciaz  2017           7.25  ...       Dealer        Manual     0\n",
              "3    wagon r  2011           2.85  ...       Dealer        Manual     0\n",
              "4      swift  2014           4.60  ...       Dealer        Manual     0\n",
              "..       ...   ...            ...  ...          ...           ...   ...\n",
              "296     city  2016           9.50  ...       Dealer        Manual     0\n",
              "297     brio  2015           4.00  ...       Dealer        Manual     0\n",
              "298     city  2009           3.35  ...       Dealer        Manual     0\n",
              "299     city  2017          11.50  ...       Dealer        Manual     0\n",
              "300     brio  2016           5.30  ...       Dealer        Manual     0\n",
              "\n",
              "[301 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KefViu1AcL-L",
        "colab_type": "code",
        "outputId": "64db4a8b-8304-413b-f62d-a2e46bc675f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(301, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ht_lbSLUdIaS",
        "colab_type": "code",
        "outputId": "b93e135d-39c0-4b39-d302-18bcc80e7520",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "n = df.shape[0]\n",
        "n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "301"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HAY4mX_rdLsy",
        "colab_type": "code",
        "outputId": "d0289e2d-a1b0-4511-96a2-708e716e8e87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "source": [
        "x = df['Present_Price']\n",
        "x"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       5.59\n",
              "1       9.54\n",
              "2       9.85\n",
              "3       4.15\n",
              "4       6.87\n",
              "       ...  \n",
              "296    11.60\n",
              "297     5.90\n",
              "298    11.00\n",
              "299    12.50\n",
              "300     5.90\n",
              "Name: Present_Price, Length: 301, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8UFDb2CkdTMR",
        "colab_type": "code",
        "outputId": "8df743ed-3931-4add-f1bb-81e97df420d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "source": [
        "y = df['Selling_Price']\n",
        "y"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       3.35\n",
              "1       4.75\n",
              "2       7.25\n",
              "3       2.85\n",
              "4       4.60\n",
              "       ...  \n",
              "296     9.50\n",
              "297     4.00\n",
              "298     3.35\n",
              "299    11.50\n",
              "300     5.30\n",
              "Name: Selling_Price, Length: 301, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oL8BDn9pdXqa",
        "colab_type": "code",
        "outputId": "819f6fd6-24e9-4535-f0d2-29b866f979a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "num = (n * sum(x * y)) - (sum(x) * sum(y))\n",
        "num"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3487326.1479599993"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TDzC2gQ7diBk",
        "colab_type": "code",
        "outputId": "96eb6b7b-afea-47d0-b7ce-8992b08a4beb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "den = (n * sum(x ** 2)) - (sum(x) ** 2)\n",
        "den"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6747282.016828"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KmWcK2gCdn-k",
        "colab_type": "code",
        "outputId": "5f1d5001-4b0c-4855-a823-155eb32db800",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "m = num / den\n",
        "m"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5168490273953963"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BTr-fZrWdqIS",
        "colab_type": "code",
        "outputId": "29c8c34a-8c38-4eb6-97d8-efa8e2280304",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "c = (sum(y) - (m * sum(x))) / n\n",
        "c"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.718527470981773"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rVLXRll6dvC7",
        "colab_type": "code",
        "outputId": "a79a2e95-9429-485b-f419-63e19fd3bbc9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "source": [
        "Y = m * x + c\n",
        "Y"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      3.607714\n",
              "1      5.649267\n",
              "2      5.809490\n",
              "3      2.863451\n",
              "4      4.269280\n",
              "         ...   \n",
              "296    6.713976\n",
              "297    3.767937\n",
              "298    6.403867\n",
              "299    7.179140\n",
              "300    3.767937\n",
              "Name: Present_Price, Length: 301, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XdNmXIoedydo",
        "colab_type": "code",
        "outputId": "c8bcfeb7-4eff-4e30-a12c-e1af54e34d8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "source": [
        "error = Y - y\n",
        "error"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      0.257714\n",
              "1      0.899267\n",
              "2     -1.440510\n",
              "3      0.013451\n",
              "4     -0.330720\n",
              "         ...   \n",
              "296   -2.786024\n",
              "297   -0.232063\n",
              "298    3.053867\n",
              "299   -4.320860\n",
              "300   -1.532063\n",
              "Length: 301, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-WWiztesd1b1",
        "colab_type": "code",
        "outputId": "5d145627-f2b2-4b1d-d9fa-1c9492ac1cd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "serror = ((sum(error)) ** 2) / n\n",
        "serror"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.8979862339600064e-27"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g9T2ybLnd5bz",
        "colab_type": "code",
        "outputId": "1fa6796e-70d5-4cc7-8e81-f3540d1ab682",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "plt.scatter(df['Present_Price'], df['Selling_Price'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f205a2a7898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "94qtb2Smd-Iu",
        "colab_type": "code",
        "outputId": "ba6f0c68-301d-4de1-f3f6-bd06ddf9a512",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "find = int(input(\"Enter the present price of car: \"))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter the present price of car: 23\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7myEG1IGeHM3",
        "colab_type": "code",
        "outputId": "fa9a9ed6-307e-4a1a-c927-a2a2c63856be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "est = m * find + c\n",
        "print(\"The selling price of car for Rs.\", find, \"as present present price is\", est)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The selling price of car for Rs. 23 as present present price is 12.606055101075887\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}