Predict_rocket_launch_delays_with_machine_learning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Predict rocket launch delays with machine learning.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMzGgzLLOAOgQfcGhNXRXtv",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/natnew/Python-Projects-Predict-Rocket-Launch-Delays-With-Machine-Learning/blob/main/Predict_rocket_launch_delays_with_machine_learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "47lMvsieitJX"
      },
      "source": [
        "#IMPORT LIBRARIES"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t4tlswUmidNY"
      },
      "source": [
        "# Pandas library is used for handling tabular data\n",
        "import pandas as pd\n",
        "\n",
        "# NumPy is used for handling numerical series operations (addition, multiplication, and ...)\n",
        "\n",
        "import numpy as np\n",
        "# Sklearn library contains all the machine learning packages we need to digest and extract patterns from the data\n",
        "from sklearn import linear_model, model_selection, metrics\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Machine learning libraries used to build a decision tree\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn import tree\n",
        "\n",
        "# Sklearn's preprocessing library is used for processing and cleaning the data \n",
        "from sklearn import preprocessing\n",
        "\n",
        "# for visualizing the tree\n",
        "import pydotplus\n",
        "from IPython.display import Image"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "18IhBr2Ni1Nh"
      },
      "source": [
        "#READ DATA INTO A VARIABLE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "AF_CX6eBihOQ",
        "outputId": "f3cfee5c-7be4-4447-f495-a1f2eeeeeb46"
      },
      "source": [
        "launch_data = pd.read_excel('RocketLaunchDataCompleted.xlsx')\n",
        "launch_data.head()"
      ],
      "execution_count": 2,
      "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>Name</th>\n",
              "      <th>Date</th>\n",
              "      <th>Time (East Coast)</th>\n",
              "      <th>Location</th>\n",
              "      <th>Crewed or Uncrewed</th>\n",
              "      <th>Launched?</th>\n",
              "      <th>High Temp</th>\n",
              "      <th>Low Temp</th>\n",
              "      <th>Ave Temp</th>\n",
              "      <th>Temp at Launch Time</th>\n",
              "      <th>Hist High Temp</th>\n",
              "      <th>Hist Low Temp</th>\n",
              "      <th>Hist Ave Temp</th>\n",
              "      <th>Percipitation at Launch Time</th>\n",
              "      <th>Hist Ave Percipitation</th>\n",
              "      <th>Wind Direction</th>\n",
              "      <th>Max Wind Speed</th>\n",
              "      <th>Visibility</th>\n",
              "      <th>Wind Speed at Launch Time</th>\n",
              "      <th>Hist Ave Max Wind Speed</th>\n",
              "      <th>Hist Ave Visibility</th>\n",
              "      <th>Sea Level Pressure</th>\n",
              "      <th>Hist Ave Sea Level Pressure</th>\n",
              "      <th>Day Length</th>\n",
              "      <th>Condition</th>\n",
              "      <th>Notes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1958-12-04</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cape Canaveral</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>75.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>71.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>75.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.08</td>\n",
              "      <td>E</td>\n",
              "      <td>16.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.22</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10:26:00</td>\n",
              "      <td>Cloudy</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1958-12-05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cape Canaveral</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>78.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>73.39</td>\n",
              "      <td>NaN</td>\n",
              "      <td>75.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.09</td>\n",
              "      <td>E</td>\n",
              "      <td>14.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10:26:00</td>\n",
              "      <td>Cloudy</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Pioneer 3</td>\n",
              "      <td>1958-12-06</td>\n",
              "      <td>01:45:00</td>\n",
              "      <td>Cape Canaveral</td>\n",
              "      <td>Uncrewed</td>\n",
              "      <td>Y</td>\n",
              "      <td>73.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>60.21</td>\n",
              "      <td>62.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.09</td>\n",
              "      <td>NE</td>\n",
              "      <td>15.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10:25:00</td>\n",
              "      <td>Cloudy</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1958-12-07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cape Canaveral</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>76.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>66.04</td>\n",
              "      <td>NaN</td>\n",
              "      <td>75.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.08</td>\n",
              "      <td>N</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10:25:00</td>\n",
              "      <td>Partly Cloudy</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1958-12-08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cape Canaveral</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>79.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>70.52</td>\n",
              "      <td>NaN</td>\n",
              "      <td>75.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.09</td>\n",
              "      <td>E</td>\n",
              "      <td>12.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.23</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12:24:00</td>\n",
              "      <td>Partly Cloudy</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Name       Date Time (East Coast)  ... Day Length      Condition Notes\n",
              "0        NaN 1958-12-04               NaN  ...   10:26:00         Cloudy   NaN\n",
              "1        NaN 1958-12-05               NaN  ...   10:26:00         Cloudy   NaN\n",
              "2  Pioneer 3 1958-12-06          01:45:00  ...   10:25:00         Cloudy   NaN\n",
              "3        NaN 1958-12-07               NaN  ...   10:25:00  Partly Cloudy   NaN\n",
              "4        NaN 1958-12-08               NaN  ...   12:24:00  Partly Cloudy   NaN\n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qn3OUtUii5As"
      },
      "source": [
        "#EXPLORE DATA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HAV-96XjikUP",
        "outputId": "fdc0a5b9-967e-4102-99a2-0011dc113df6"
      },
      "source": [
        "launch_data.columns"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Name', 'Date', 'Time (East Coast)', 'Location', 'Crewed or Uncrewed',\n",
              "       'Launched?', 'High Temp', 'Low Temp', 'Ave Temp', 'Temp at Launch Time',\n",
              "       'Hist High Temp', 'Hist Low Temp', 'Hist Ave Temp',\n",
              "       'Percipitation at Launch Time', 'Hist Ave Percipitation',\n",
              "       'Wind Direction', 'Max Wind Speed', 'Visibility',\n",
              "       'Wind Speed at Launch Time', 'Hist Ave Max Wind Speed',\n",
              "       'Hist Ave Visibility', 'Sea Level Pressure',\n",
              "       'Hist Ave Sea Level Pressure', 'Day Length', 'Condition', 'Notes'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J-DKipi-jk4h"
      },
      "source": [
        "#DATA CLEANING"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bY0OBTJkjng7",
        "outputId": "b47efaeb-eea4-4a85-8208-e8f354afd812"
      },
      "source": [
        "launch_data.info()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 300 entries, 0 to 299\n",
            "Data columns (total 26 columns):\n",
            " #   Column                        Non-Null Count  Dtype         \n",
            "---  ------                        --------------  -----         \n",
            " 0   Name                          60 non-null     object        \n",
            " 1   Date                          300 non-null    datetime64[ns]\n",
            " 2   Time (East Coast)             59 non-null     object        \n",
            " 3   Location                      300 non-null    object        \n",
            " 4   Crewed or Uncrewed            60 non-null     object        \n",
            " 5   Launched?                     60 non-null     object        \n",
            " 6   High Temp                     299 non-null    float64       \n",
            " 7   Low Temp                      299 non-null    float64       \n",
            " 8   Ave Temp                      299 non-null    float64       \n",
            " 9   Temp at Launch Time           59 non-null     float64       \n",
            " 10  Hist High Temp                299 non-null    float64       \n",
            " 11  Hist Low Temp                 299 non-null    float64       \n",
            " 12  Hist Ave Temp                 299 non-null    float64       \n",
            " 13  Percipitation at Launch Time  299 non-null    float64       \n",
            " 14  Hist Ave Percipitation        299 non-null    float64       \n",
            " 15  Wind Direction                299 non-null    object        \n",
            " 16  Max Wind Speed                299 non-null    float64       \n",
            " 17  Visibility                    299 non-null    float64       \n",
            " 18  Wind Speed at Launch Time     59 non-null     float64       \n",
            " 19  Hist Ave Max Wind Speed       0 non-null      float64       \n",
            " 20  Hist Ave Visibility           0 non-null      float64       \n",
            " 21  Sea Level Pressure            299 non-null    object        \n",
            " 22  Hist Ave Sea Level Pressure   0 non-null      float64       \n",
            " 23  Day Length                    298 non-null    object        \n",
            " 24  Condition                     298 non-null    object        \n",
            " 25  Notes                         3 non-null      object        \n",
            "dtypes: datetime64[ns](1), float64(15), object(10)\n",
            "memory usage: 61.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "k5slCKkLkNfX",
        "outputId": "fb040d98-b1ff-4a51-bc8a-032e6bed0af8"
      },
      "source": [
        "## To handle missing values, we will fill the missing values with appropriate values \n",
        "launch_data['Launched?'].fillna('N',inplace=True)\n",
        "launch_data['Crewed or Uncrewed'].fillna('Uncrewed',inplace=True)\n",
        "launch_data['Wind Direction'].fillna('unknown',inplace=True)\n",
        "launch_data['Condition'].fillna('Fair',inplace=True)\n",
        "launch_data.fillna(0,inplace=True)\n",
        "launch_data.head()"
      ],
      "execution_count": 5,
      "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>Name</th>\n",
              "      <th>Date</th>\n",
              "      <th>Time (East Coast)</th>\n",
              "      <th>Location</th>\n",
              "      <th>Crewed or Uncrewed</th>\n",
              "      <th>Launched?</th>\n",
              "      <th>High Temp</th>\n",
              "      <th>Low Temp</th>\n",
              "      <th>Ave Temp</th>\n",
              "      <th>Temp at Launch Time</th>\n",
              "      <th>Hist High Temp</th>\n",
              "      <th>Hist Low Temp</th>\n",
              "      <th>Hist Ave Temp</th>\n",
              "      <th>Percipitation at Launch Time</th>\n",
              "      <th>Hist Ave Percipitation</th>\n",
              "      <th>Wind Direction</th>\n",
              "      <th>Max Wind Speed</th>\n",
              "      <th>Visibility</th>\n",
              "      <th>Wind Speed at Launch Time</th>\n",
              "      <th>Hist Ave Max Wind Speed</th>\n",
              "      <th>Hist Ave Visibility</th>\n",
              "      <th>Sea Level Pressure</th>\n",
              "      <th>Hist Ave Sea Level Pressure</th>\n",
              "      <th>Day Length</th>\n",
              "      <th>Condition</th>\n",
              "      <th>Notes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1958-12-04</td>\n",
              "      <td>0</td>\n",
              "      <td>Cape Canaveral</td>\n",
              "      <td>Uncrewed</td>\n",
              "      <td>N</td>\n",
              "      <td>75.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>71.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.08</td>\n",
              "      <td>E</td>\n",
              "      <td>16.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.22</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10:26:00</td>\n",
              "      <td>Cloudy</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1958-12-05</td>\n",
              "      <td>0</td>\n",
              "      <td>Cape Canaveral</td>\n",
              "      <td>Uncrewed</td>\n",
              "      <td>N</td>\n",
              "      <td>78.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>73.39</td>\n",
              "      <td>0.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.09</td>\n",
              "      <td>E</td>\n",
              "      <td>14.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10:26:00</td>\n",
              "      <td>Cloudy</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Pioneer 3</td>\n",
              "      <td>1958-12-06</td>\n",
              "      <td>01:45:00</td>\n",
              "      <td>Cape Canaveral</td>\n",
              "      <td>Uncrewed</td>\n",
              "      <td>Y</td>\n",
              "      <td>73.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>60.21</td>\n",
              "      <td>62.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.09</td>\n",
              "      <td>NE</td>\n",
              "      <td>15.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10:25:00</td>\n",
              "      <td>Cloudy</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1958-12-07</td>\n",
              "      <td>0</td>\n",
              "      <td>Cape Canaveral</td>\n",
              "      <td>Uncrewed</td>\n",
              "      <td>N</td>\n",
              "      <td>76.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>66.04</td>\n",
              "      <td>0.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.08</td>\n",
              "      <td>N</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.28</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10:25:00</td>\n",
              "      <td>Partly Cloudy</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1958-12-08</td>\n",
              "      <td>0</td>\n",
              "      <td>Cape Canaveral</td>\n",
              "      <td>Uncrewed</td>\n",
              "      <td>N</td>\n",
              "      <td>79.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>70.52</td>\n",
              "      <td>0.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.09</td>\n",
              "      <td>E</td>\n",
              "      <td>12.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.23</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12:24:00</td>\n",
              "      <td>Partly Cloudy</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Name       Date Time (East Coast)  ... Day Length      Condition Notes\n",
              "0          0 1958-12-04                 0  ...   10:26:00         Cloudy     0\n",
              "1          0 1958-12-05                 0  ...   10:26:00         Cloudy     0\n",
              "2  Pioneer 3 1958-12-06          01:45:00  ...   10:25:00         Cloudy     0\n",
              "3          0 1958-12-07                 0  ...   10:25:00  Partly Cloudy     0\n",
              "4          0 1958-12-08                 0  ...   12:24:00  Partly Cloudy     0\n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Y385a_ekdKp"
      },
      "source": [
        "#SEE CHANGES MADE TO THE DATA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kbqeF_gOkXMi",
        "outputId": "cd9c4743-46b3-4098-a4db-ac9c6c3bb93f"
      },
      "source": [
        "launch_data.info()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 300 entries, 0 to 299\n",
            "Data columns (total 26 columns):\n",
            " #   Column                        Non-Null Count  Dtype         \n",
            "---  ------                        --------------  -----         \n",
            " 0   Name                          300 non-null    object        \n",
            " 1   Date                          300 non-null    datetime64[ns]\n",
            " 2   Time (East Coast)             300 non-null    object        \n",
            " 3   Location                      300 non-null    object        \n",
            " 4   Crewed or Uncrewed            300 non-null    object        \n",
            " 5   Launched?                     300 non-null    object        \n",
            " 6   High Temp                     300 non-null    float64       \n",
            " 7   Low Temp                      300 non-null    float64       \n",
            " 8   Ave Temp                      300 non-null    float64       \n",
            " 9   Temp at Launch Time           300 non-null    float64       \n",
            " 10  Hist High Temp                300 non-null    float64       \n",
            " 11  Hist Low Temp                 300 non-null    float64       \n",
            " 12  Hist Ave Temp                 300 non-null    float64       \n",
            " 13  Percipitation at Launch Time  300 non-null    float64       \n",
            " 14  Hist Ave Percipitation        300 non-null    float64       \n",
            " 15  Wind Direction                300 non-null    object        \n",
            " 16  Max Wind Speed                300 non-null    float64       \n",
            " 17  Visibility                    300 non-null    float64       \n",
            " 18  Wind Speed at Launch Time     300 non-null    float64       \n",
            " 19  Hist Ave Max Wind Speed       300 non-null    float64       \n",
            " 20  Hist Ave Visibility           300 non-null    float64       \n",
            " 21  Sea Level Pressure            300 non-null    object        \n",
            " 22  Hist Ave Sea Level Pressure   300 non-null    float64       \n",
            " 23  Day Length                    300 non-null    object        \n",
            " 24  Condition                     300 non-null    object        \n",
            " 25  Notes                         300 non-null    object        \n",
            "dtypes: datetime64[ns](1), float64(15), object(10)\n",
            "memory usage: 61.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1PnGc4apkucV"
      },
      "source": [
        "#DATA MANIPULATION"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KWT5p5kTkxpm"
      },
      "source": [
        "## As part of the data cleaning process, we have to convert text data to numerical because computers understand only numbers\n",
        "label_encoder = preprocessing.LabelEncoder()\n",
        "\n",
        "# Three columns have categorical text info, and we convert them to numbers\n",
        "launch_data['Crewed or Uncrewed'] = label_encoder.fit_transform(launch_data['Crewed or Uncrewed'])\n",
        "launch_data['Wind Direction'] = label_encoder.fit_transform(launch_data['Wind Direction'])\n",
        "launch_data['Condition'] = label_encoder.fit_transform(launch_data['Condition'])"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "Qijf81zFk2V2",
        "outputId": "2fb2a25d-e2cb-4a95-df0b-c7928a262944"
      },
      "source": [
        "launch_data.head()"
      ],
      "execution_count": 8,
      "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>Name</th>\n",
              "      <th>Date</th>\n",
              "      <th>Time (East Coast)</th>\n",
              "      <th>Location</th>\n",
              "      <th>Crewed or Uncrewed</th>\n",
              "      <th>Launched?</th>\n",
              "      <th>High Temp</th>\n",
              "      <th>Low Temp</th>\n",
              "      <th>Ave Temp</th>\n",
              "      <th>Temp at Launch Time</th>\n",
              "      <th>Hist High Temp</th>\n",
              "      <th>Hist Low Temp</th>\n",
              "      <th>Hist Ave Temp</th>\n",
              "      <th>Percipitation at Launch Time</th>\n",
              "      <th>Hist Ave Percipitation</th>\n",
              "      <th>Wind Direction</th>\n",
              "      <th>Max Wind Speed</th>\n",
              "      <th>Visibility</th>\n",
              "      <th>Wind Speed at Launch Time</th>\n",
              "      <th>Hist Ave Max Wind Speed</th>\n",
              "      <th>Hist Ave Visibility</th>\n",
              "      <th>Sea Level Pressure</th>\n",
              "      <th>Hist Ave Sea Level Pressure</th>\n",
              "      <th>Day Length</th>\n",
              "      <th>Condition</th>\n",
              "      <th>Notes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1958-12-04</td>\n",
              "      <td>0</td>\n",
              "      <td>Cape Canaveral</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>75.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>71.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.22</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10:26:00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1958-12-05</td>\n",
              "      <td>0</td>\n",
              "      <td>Cape Canaveral</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>78.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>73.39</td>\n",
              "      <td>0.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10:26:00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Pioneer 3</td>\n",
              "      <td>1958-12-06</td>\n",
              "      <td>01:45:00</td>\n",
              "      <td>Cape Canaveral</td>\n",
              "      <td>1</td>\n",
              "      <td>Y</td>\n",
              "      <td>73.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>60.21</td>\n",
              "      <td>62.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.09</td>\n",
              "      <td>2</td>\n",
              "      <td>15.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10:25:00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1958-12-07</td>\n",
              "      <td>0</td>\n",
              "      <td>Cape Canaveral</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>76.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>66.04</td>\n",
              "      <td>0.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.08</td>\n",
              "      <td>1</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.28</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10:25:00</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1958-12-08</td>\n",
              "      <td>0</td>\n",
              "      <td>Cape Canaveral</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>79.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>70.52</td>\n",
              "      <td>0.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.23</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12:24:00</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Name       Date Time (East Coast)  ... Day Length  Condition Notes\n",
              "0          0 1958-12-04                 0  ...   10:26:00          0     0\n",
              "1          0 1958-12-05                 0  ...   10:26:00          0     0\n",
              "2  Pioneer 3 1958-12-06          01:45:00  ...   10:25:00          0     0\n",
              "3          0 1958-12-07                 0  ...   10:25:00          6     0\n",
              "4          0 1958-12-08                 0  ...   12:24:00          6     0\n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5UGc7SCyrXov"
      },
      "source": [
        "#FURTHER CLEANING"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D2CHcQgFrZTO"
      },
      "source": [
        "# First, we save the output we are interested in. In this case, \"launch\" yes and no's go into the output variable.\n",
        "y = launch_data['Launched?']\n",
        "\n",
        "# Removing the columns we are not interested in\n",
        "launch_data.drop(['Name','Date','Time (East Coast)','Location','Launched?','Hist Ave Sea Level Pressure','Sea Level Pressure','Day Length','Notes','Hist Ave Visibility', 'Hist Ave Max Wind Speed'],axis=1, inplace=True)\n",
        "\n",
        "# Saving the rest of the data as input data\n",
        "X = launch_data"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u4Fxtw58rlZm",
        "outputId": "1bfe60cf-14a7-44c9-9c72-54f201765b80"
      },
      "source": [
        "# List of variables that our machine learning algorithm is going to look at:\n",
        "X.columns"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Crewed or Uncrewed', 'High Temp', 'Low Temp', 'Ave Temp',\n",
              "       'Temp at Launch Time', 'Hist High Temp', 'Hist Low Temp',\n",
              "       'Hist Ave Temp', 'Percipitation at Launch Time',\n",
              "       'Hist Ave Percipitation', 'Wind Direction', 'Max Wind Speed',\n",
              "       'Visibility', 'Wind Speed at Launch Time', 'Condition'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k9Gak8bzruyh"
      },
      "source": [
        "Can you predict whether a launch is likely to happen given specific weather conditions? "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BKZkg_dhr0mJ"
      },
      "source": [
        "#CREATE A MACHINE LEARNING MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gSxL_yWOrvx4"
      },
      "source": [
        "# Create decision tree classifier \n",
        "tree_model = DecisionTreeClassifier(random_state=0,max_depth=5)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RatVencFsG6q"
      },
      "source": [
        "#SPLIT DATA INTO TRAIN AND TEST DATA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GhZ0M-qlsLQE"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2, random_state=99)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RynVP5FCsO0i"
      },
      "source": [
        "#FIT THE MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vV5U3MzxsQLi",
        "outputId": "7dd624df-0987-4f33-ae66-8740af6f0735"
      },
      "source": [
        "# Fitting the model to the training data\n",
        "tree_model.fit(X_train,y_train)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=5, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=0, splitter='best')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wym4o5slsUIR"
      },
      "source": [
        "#TEST THE MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qNKuiC2dsVvQ",
        "outputId": "d1c7848f-0956-4d82-dab2-4f1a89cead33"
      },
      "source": [
        "# Do prediction on test Data\n",
        "y_pred = tree_model.predict(X_test)\n",
        "print(y_pred)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['N' 'N' 'N' 'Y' 'N' 'N' 'N' 'N' 'Y' 'Y' 'N' 'Y' 'N' 'N' 'N' 'N' 'N' 'N'\n",
            " 'N' 'N' 'N' 'N' 'Y' 'Y' 'N' 'N' 'N' 'N' 'N' 'N' 'N' 'Y' 'N' 'N' 'N' 'N'\n",
            " 'N' 'N' 'N' 'N' 'N' 'N' 'Y' 'N' 'N' 'N' 'Y' 'N' 'N' 'N' 'N' 'N' 'N' 'N'\n",
            " 'N' 'N' 'N' 'N' 'N' 'Y']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FtN3CyyCsZR2"
      },
      "source": [
        "#SCORE THE MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7nax2RnYsasJ",
        "outputId": "2d549047-1bbb-4d7f-b4fe-6f46e040764a"
      },
      "source": [
        "# Calculate accuracy\n",
        "tree_model.score(X_test,y_test)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9833333333333333"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sOeM7lu7sjTk"
      },
      "source": [
        "#VISUALISE THE MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ilh8pSdUsleC"
      },
      "source": [
        "# Let's import a library for visualizing our decision tree.\n",
        "from sklearn.tree import export_graphviz\n",
        "\n",
        "def tree_graph_to_png(tree, feature_names,class_names, png_file_to_save):\n",
        "    tree_str = export_graphviz(tree, feature_names=feature_names, class_names=class_names,\n",
        "                                     filled=True, out_file=None)\n",
        "    graph = pydotplus.graph_from_dot_data(tree_str)  \n",
        "    return Image(graph.create_png())"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "id": "vxxJ-zsGsn-y",
        "outputId": "8ed373fe-7a11-4614-a638-16e5721f8340"
      },
      "source": [
        "# This function takes a machine learning model and visualizes it.\n",
        "tree_graph_to_png(tree=tree_model, feature_names=X.columns.values,class_names=['No Launch','Launch'], png_file_to_save='decision-tree.png')"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "831TSsA1svge",
        "outputId": "687ff5a6-5ac2-4199-aefc-050397477f30"
      },
      "source": [
        "launch_data[(launch_data['Wind Speed at Launch Time'] > 1) & (launch_data['Max Wind Speed'] > 30.5)]"
      ],
      "execution_count": 18,
      "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>Crewed or Uncrewed</th>\n",
              "      <th>High Temp</th>\n",
              "      <th>Low Temp</th>\n",
              "      <th>Ave Temp</th>\n",
              "      <th>Temp at Launch Time</th>\n",
              "      <th>Hist High Temp</th>\n",
              "      <th>Hist Low Temp</th>\n",
              "      <th>Hist Ave Temp</th>\n",
              "      <th>Percipitation at Launch Time</th>\n",
              "      <th>Hist Ave Percipitation</th>\n",
              "      <th>Wind Direction</th>\n",
              "      <th>Max Wind Speed</th>\n",
              "      <th>Visibility</th>\n",
              "      <th>Wind Speed at Launch Time</th>\n",
              "      <th>Condition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>292</th>\n",
              "      <td>0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>75.02</td>\n",
              "      <td>74.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.15</td>\n",
              "      <td>4</td>\n",
              "      <td>32.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Crewed or Uncrewed  High Temp  ...  Wind Speed at Launch Time  Condition\n",
              "292                   0       86.0  ...                       25.0          2\n",
              "\n",
              "[1 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oZ64CJyXs5L8"
      },
      "source": [
        "#TEST THE MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2NZD71ujs6dO",
        "outputId": "fbf56493-3a18-4616-d697-489f280be46c"
      },
      "source": [
        "# ['Crewed or Uncrewed', 'High Temp', 'Low Temp', 'Ave Temp',\n",
        "#        'Temp at Launch Time', 'Hist High Temp', 'Hist Low Temp',\n",
        "#        'Hist Ave Temp', 'Precipitation at Launch Time',\n",
        "#        'Hist Ave Precipitation', 'Wind Direction', 'Max Wind Speed',\n",
        "#        'Visibility', 'Wind Speed at Launch Time', 'Hist Ave Max Wind Speed',\n",
        "#        'Hist Ave Visibility', 'Condition']\n",
        "\n",
        "data_input = [ 1.  , 75.  , 68.  , 71.  ,  0.  , 75.  , 55.  , 65.  ,  0.  , 0.08,  0.  , 16.  , 15.  ,  0.  ,  0. ]\n",
        "\n",
        "tree_model.predict([data_input])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['N'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    }
  ]
}