ml model

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:02:08.520Z",
          "iopub.execute_input": "2021-04-25T12:02:08.557Z",
          "shell.execute_reply": "2021-04-25T12:02:08.928Z",
          "iopub.status.idle": "2021-04-25T12:02:08.896Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"autism_screening.csv\")"
      ],
      "outputs": [],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:02:10.219Z",
          "iopub.execute_input": "2021-04-25T12:02:10.271Z",
          "iopub.status.idle": "2021-04-25T12:02:10.320Z",
          "shell.execute_reply": "2021-04-25T12:02:10.339Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "   A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  A7_Score  \\\n0         1         1         1         1         0         0         1   \n1         1         1         0         1         0         0         0   \n2         1         1         0         1         1         0         1   \n3         1         1         0         1         0         0         1   \n4         1         0         0         0         0         0         0   \n\n   A8_Score  A9_Score  A10_Score  ...  gender       ethnicity jundice austim  \\\n0         1         0          0  ...       f  White-European      no     no   \n1         1         0          1  ...       m          Latino      no    yes   \n2         1         1          1  ...       m          Latino     yes    yes   \n3         1         0          1  ...       f  White-European      no    yes   \n4         1         0          0  ...       f               ?      no     no   \n\n   contry_of_res used_app_before result     age_desc relation Class/ASD  \n0  United States              no    6.0  18 and more     Self        NO  \n1         Brazil              no    5.0  18 and more     Self        NO  \n2          Spain              no    8.0  18 and more   Parent       YES  \n3  United States              no    6.0  18 and more     Self        NO  \n4          Egypt              no    2.0  18 and more        ?        NO  \n\n[5 rows x 21 columns]",
            "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>A1_Score</th>\n      <th>A2_Score</th>\n      <th>A3_Score</th>\n      <th>A4_Score</th>\n      <th>A5_Score</th>\n      <th>A6_Score</th>\n      <th>A7_Score</th>\n      <th>A8_Score</th>\n      <th>A9_Score</th>\n      <th>A10_Score</th>\n      <th>...</th>\n      <th>gender</th>\n      <th>ethnicity</th>\n      <th>jundice</th>\n      <th>austim</th>\n      <th>contry_of_res</th>\n      <th>used_app_before</th>\n      <th>result</th>\n      <th>age_desc</th>\n      <th>relation</th>\n      <th>Class/ASD</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>f</td>\n      <td>White-European</td>\n      <td>no</td>\n      <td>no</td>\n      <td>United States</td>\n      <td>no</td>\n      <td>6.0</td>\n      <td>18 and more</td>\n      <td>Self</td>\n      <td>NO</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>m</td>\n      <td>Latino</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>Brazil</td>\n      <td>no</td>\n      <td>5.0</td>\n      <td>18 and more</td>\n      <td>Self</td>\n      <td>NO</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>...</td>\n      <td>m</td>\n      <td>Latino</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>Spain</td>\n      <td>no</td>\n      <td>8.0</td>\n      <td>18 and more</td>\n      <td>Parent</td>\n      <td>YES</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>f</td>\n      <td>White-European</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>United States</td>\n      <td>no</td>\n      <td>6.0</td>\n      <td>18 and more</td>\n      <td>Self</td>\n      <td>NO</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>f</td>\n      <td>?</td>\n      <td>no</td>\n      <td>no</td>\n      <td>Egypt</td>\n      <td>no</td>\n      <td>2.0</td>\n      <td>18 and more</td>\n      <td>?</td>\n      <td>NO</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 21 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:02:11.109Z",
          "iopub.execute_input": "2021-04-25T12:02:11.141Z",
          "iopub.status.idle": "2021-04-25T12:02:11.189Z",
          "shell.execute_reply": "2021-04-25T12:02:11.208Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 704 entries, 0 to 703\n",
            "Data columns (total 21 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   A1_Score         704 non-null    int64  \n",
            " 1   A2_Score         704 non-null    int64  \n",
            " 2   A3_Score         704 non-null    int64  \n",
            " 3   A4_Score         704 non-null    int64  \n",
            " 4   A5_Score         704 non-null    int64  \n",
            " 5   A6_Score         704 non-null    int64  \n",
            " 6   A7_Score         704 non-null    int64  \n",
            " 7   A8_Score         704 non-null    int64  \n",
            " 8   A9_Score         704 non-null    int64  \n",
            " 9   A10_Score        704 non-null    int64  \n",
            " 10  age              702 non-null    float64\n",
            " 11  gender           704 non-null    object \n",
            " 12  ethnicity        704 non-null    object \n",
            " 13  jundice          704 non-null    object \n",
            " 14  austim           704 non-null    object \n",
            " 15  contry_of_res    704 non-null    object \n",
            " 16  used_app_before  704 non-null    object \n",
            " 17  result           704 non-null    float64\n",
            " 18  age_desc         704 non-null    object \n",
            " 19  relation         704 non-null    object \n",
            " 20  Class/ASD        704 non-null    object \n",
            "dtypes: float64(2), int64(10), object(9)\n",
            "memory usage: 115.6+ KB\n"
          ]
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:51.247Z",
          "iopub.execute_input": "2021-03-02T11:28:51.258Z",
          "iopub.status.idle": "2021-03-02T11:28:51.276Z",
          "shell.execute_reply": "2021-03-02T11:28:50.876Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We can see that attributes gender, ethnicity, jaundice, autism, country of residence, used app before, age_desc, relation are object type.\n"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:51.293Z",
          "iopub.execute_input": "2021-03-02T11:28:51.304Z",
          "iopub.status.idle": "2021-03-02T11:28:51.315Z",
          "shell.execute_reply": "2021-03-02T11:28:50.888Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"gender\"].value_counts()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": "m    367\nf    337\nName: gender, dtype: int64"
          },
          "metadata": {}
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:51.331Z",
          "iopub.execute_input": "2021-03-02T11:28:51.345Z",
          "iopub.status.idle": "2021-03-02T11:28:51.363Z",
          "shell.execute_reply": "2021-03-02T11:28:50.900Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"ethnicity\"].value_counts()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": "White-European     233\nAsian              123\n?                   95\nMiddle Eastern      92\nBlack               43\nSouth Asian         36\nOthers              30\nLatino              20\nHispanic            13\nPasifika            12\nTurkish              6\nothers               1\nName: ethnicity, dtype: int64"
          },
          "metadata": {}
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:51.381Z",
          "iopub.execute_input": "2021-03-02T11:28:51.392Z",
          "iopub.status.idle": "2021-03-02T11:28:51.411Z",
          "shell.execute_reply": "2021-03-02T11:28:50.912Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"contry_of_res\"].value_counts()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": "United States           113\nUnited Arab Emirates     82\nIndia                    81\nNew Zealand              81\nUnited Kingdom           77\n                       ... \nCzech Republic            1\nTonga                     1\nCyprus                    1\nIraq                      1\nNicaragua                 1\nName: contry_of_res, Length: 67, dtype: int64"
          },
          "metadata": {}
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:51.429Z",
          "iopub.execute_input": "2021-03-02T11:28:51.440Z",
          "iopub.status.idle": "2021-03-02T11:28:51.457Z",
          "shell.execute_reply": "2021-03-02T11:28:50.925Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"age_desc\"].value_counts()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": "18 and more    704\nName: age_desc, dtype: int64"
          },
          "metadata": {}
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:51.474Z",
          "iopub.execute_input": "2021-03-02T11:28:51.484Z",
          "iopub.status.idle": "2021-03-02T11:28:51.500Z",
          "shell.execute_reply": "2021-03-02T11:28:50.936Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.nunique()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": "A1_Score            2\nA2_Score            2\nA3_Score            2\nA4_Score            2\nA5_Score            2\nA6_Score            2\nA7_Score            2\nA8_Score            2\nA9_Score            2\nA10_Score           2\nage                46\ngender              2\nethnicity          12\njundice             2\naustim              2\ncontry_of_res      67\nused_app_before     2\nresult             11\nage_desc            1\nrelation            6\nClass/ASD           2\ndtype: int64"
          },
          "metadata": {}
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:51.517Z",
          "iopub.execute_input": "2021-03-02T11:28:51.527Z",
          "iopub.status.idle": "2021-03-02T11:28:51.546Z",
          "shell.execute_reply": "2021-03-02T11:28:50.947Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#The result column is just sum of values of A1 through A10, I want my model to learn this dependency itself so I am removing this field and used app before and relation have no part in training so I am removing these fields also. Also age desc is also 1 value so its of no importance as well."
      ],
      "outputs": [],
      "execution_count": 11,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:51.562Z",
          "iopub.execute_input": "2021-03-02T11:28:51.573Z",
          "iopub.status.idle": "2021-03-02T11:28:51.586Z",
          "shell.execute_reply": "2021-03-02T11:28:50.958Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(columns=['age_desc'], inplace = True)\n"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": true
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:02:22.854Z",
          "iopub.execute_input": "2021-04-25T12:02:22.896Z",
          "iopub.status.idle": "2021-04-25T12:02:22.941Z",
          "shell.execute_reply": "2021-04-25T12:02:22.962Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(columns=['result'], inplace = True)\n",
        "df.drop(columns=['relation'], inplace = True)\n",
        "df.drop(columns=['used_app_before'], inplace = True)"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:02:24.068Z",
          "iopub.execute_input": "2021-04-25T12:02:24.102Z",
          "iopub.status.idle": "2021-04-25T12:02:24.148Z",
          "shell.execute_reply": "2021-04-25T12:02:24.172Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": "         A1_Score    A2_Score    A3_Score    A4_Score    A5_Score    A6_Score  \\\ncount  704.000000  704.000000  704.000000  704.000000  704.000000  704.000000   \nmean     0.721591    0.453125    0.457386    0.495739    0.498580    0.284091   \nstd      0.448535    0.498152    0.498535    0.500337    0.500353    0.451301   \nmin      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n50%      1.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n75%      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \nmax      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n\n         A7_Score    A8_Score    A9_Score   A10_Score         age  \ncount  704.000000  704.000000  704.000000  704.000000  702.000000  \nmean     0.417614    0.649148    0.323864    0.573864   29.698006  \nstd      0.493516    0.477576    0.468281    0.494866   16.507465  \nmin      0.000000    0.000000    0.000000    0.000000   17.000000  \n25%      0.000000    0.000000    0.000000    0.000000   21.000000  \n50%      0.000000    1.000000    0.000000    1.000000   27.000000  \n75%      1.000000    1.000000    1.000000    1.000000   35.000000  \nmax      1.000000    1.000000    1.000000    1.000000  383.000000  ",
            "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>A1_Score</th>\n      <th>A2_Score</th>\n      <th>A3_Score</th>\n      <th>A4_Score</th>\n      <th>A5_Score</th>\n      <th>A6_Score</th>\n      <th>A7_Score</th>\n      <th>A8_Score</th>\n      <th>A9_Score</th>\n      <th>A10_Score</th>\n      <th>age</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>704.000000</td>\n      <td>704.000000</td>\n      <td>704.000000</td>\n      <td>704.000000</td>\n      <td>704.000000</td>\n      <td>704.000000</td>\n      <td>704.000000</td>\n      <td>704.000000</td>\n      <td>704.000000</td>\n      <td>704.000000</td>\n      <td>702.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.721591</td>\n      <td>0.453125</td>\n      <td>0.457386</td>\n      <td>0.495739</td>\n      <td>0.498580</td>\n      <td>0.284091</td>\n      <td>0.417614</td>\n      <td>0.649148</td>\n      <td>0.323864</td>\n      <td>0.573864</td>\n      <td>29.698006</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.448535</td>\n      <td>0.498152</td>\n      <td>0.498535</td>\n      <td>0.500337</td>\n      <td>0.500353</td>\n      <td>0.451301</td>\n      <td>0.493516</td>\n      <td>0.477576</td>\n      <td>0.468281</td>\n      <td>0.494866</td>\n      <td>16.507465</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>17.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>21.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>27.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>35.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>383.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 14,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:51.677Z",
          "iopub.execute_input": "2021-03-02T11:28:51.687Z",
          "iopub.status.idle": "2021-03-02T11:28:51.706Z",
          "shell.execute_reply": "2021-03-02T11:28:51.000Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Only A1 A8 and A10 has 50% value as 1 rest all has 0.\n",
        "#These marks 25%, 50%, 75% rows show the corresponding percentiles: a percentile indicates #the value below which a given percentage of observations in a group of observations fall\n",
        "#Note that the answer to all questions A1 to A10 were binary 0 or 1 and 50% value being 1 denotes that in comparision to other questions people aanaswered 1 on these 3 questions.\n",
        "#The mean age is 29"
      ],
      "outputs": [],
      "execution_count": 15,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:51.722Z",
          "iopub.execute_input": "2021-03-02T11:28:51.733Z",
          "iopub.status.idle": "2021-03-02T11:28:51.744Z",
          "shell.execute_reply": "2021-03-02T11:28:51.011Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "df.hist(bins = 50, figsize = (20,15))\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1440x1080 with 12 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 16,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:51.761Z",
          "iopub.execute_input": "2021-03-02T11:28:51.772Z",
          "iopub.status.idle": "2021-03-02T11:28:52.317Z",
          "shell.execute_reply": "2021-03-02T11:28:52.206Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#It is clearly visible that the result shaped column is kind of a bell shaped graph i.e autism distribution is roughly Normal\n",
        "#Each question has different distribution of Yes No values. Only A5 is looking to have a 50-50 slpit."
      ],
      "outputs": [],
      "execution_count": 17,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2021-03-02T11:28:52.215Z",
          "iopub.status.busy": "2021-03-02T11:28:52.335Z",
          "iopub.execute_input": "2021-03-02T11:28:52.348Z",
          "iopub.status.idle": "2021-03-02T11:28:52.360Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "df.replace(\"?\",np.nan,inplace=True)\n"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:02:35.038Z",
          "iopub.execute_input": "2021-04-25T12:02:35.075Z",
          "iopub.status.idle": "2021-04-25T12:02:35.142Z",
          "shell.execute_reply": "2021-04-25T12:02:35.168Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.rename(columns={'austim': 'relative_autism'}, inplace=True)\n",
        "df.rename(columns={'Class/ASD': 'asd_label'}, inplace=True)\n",
        "df.rename(columns={'jundice': 'juandice'}, inplace=True)\n"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:02:36.396Z",
          "iopub.execute_input": "2021-04-25T12:02:36.438Z",
          "iopub.status.idle": "2021-04-25T12:02:36.493Z",
          "shell.execute_reply": "2021-04-25T12:02:36.525Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.juandice = df.juandice.apply(lambda x: 0 if x == 'no' else 1)\n",
        "df.relative_autism = df.relative_autism.apply(lambda x: 0 if x == 'no' else 1)\n"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:02:39.067Z",
          "iopub.execute_input": "2021-04-25T12:02:39.101Z",
          "iopub.status.idle": "2021-04-25T12:02:39.145Z",
          "shell.execute_reply": "2021-04-25T12:02:39.175Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.asd_label = df.asd_label.apply(lambda x: 0 if x == 'NO' else 1)\n"
      ],
      "outputs": [],
      "execution_count": 9,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:02:39.888Z",
          "iopub.execute_input": "2021-04-25T12:02:39.918Z",
          "iopub.status.idle": "2021-04-25T12:02:39.973Z",
          "shell.execute_reply": "2021-04-25T12:02:40.006Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.gender = df.gender.apply(lambda x : 0 if x == 'm' else 1)"
      ],
      "outputs": [],
      "execution_count": 10,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:02:40.912Z",
          "iopub.execute_input": "2021-04-25T12:02:40.944Z",
          "iopub.status.idle": "2021-04-25T12:02:40.989Z",
          "shell.execute_reply": "2021-04-25T12:02:41.022Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 23,
          "data": {
            "text/plain": "   A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  A7_Score  \\\n0         1         1         1         1         0         0         1   \n1         1         1         0         1         0         0         0   \n2         1         1         0         1         1         0         1   \n3         1         1         0         1         0         0         1   \n4         1         0         0         0         0         0         0   \n\n   A8_Score  A9_Score  A10_Score   age  gender       ethnicity  juandice  \\\n0         1         0          0  26.0       1  White-European         0   \n1         1         0          1  24.0       0          Latino         0   \n2         1         1          1  27.0       0          Latino         1   \n3         1         0          1  35.0       1  White-European         0   \n4         1         0          0  40.0       1             NaN         0   \n\n   relative_autism  contry_of_res  asd_label  \n0                0  United States          0  \n1                1         Brazil          0  \n2                1          Spain          1  \n3                1  United States          0  \n4                0          Egypt          0  ",
            "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>A1_Score</th>\n      <th>A2_Score</th>\n      <th>A3_Score</th>\n      <th>A4_Score</th>\n      <th>A5_Score</th>\n      <th>A6_Score</th>\n      <th>A7_Score</th>\n      <th>A8_Score</th>\n      <th>A9_Score</th>\n      <th>A10_Score</th>\n      <th>age</th>\n      <th>gender</th>\n      <th>ethnicity</th>\n      <th>juandice</th>\n      <th>relative_autism</th>\n      <th>contry_of_res</th>\n      <th>asd_label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>26.0</td>\n      <td>1</td>\n      <td>White-European</td>\n      <td>0</td>\n      <td>0</td>\n      <td>United States</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>24.0</td>\n      <td>0</td>\n      <td>Latino</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Brazil</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>27.0</td>\n      <td>0</td>\n      <td>Latino</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Spain</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>35.0</td>\n      <td>1</td>\n      <td>White-European</td>\n      <td>0</td>\n      <td>1</td>\n      <td>United States</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40.0</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Egypt</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 23,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:52.582Z",
          "iopub.execute_input": "2021-03-02T11:28:52.594Z",
          "iopub.status.idle": "2021-03-02T11:28:52.612Z",
          "shell.execute_reply": "2021-03-02T11:28:52.299Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.ethnicity[df.asd_label == 1].value_counts().plot(kind='bar')\n"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 24,
          "data": {
            "text/plain": "<AxesSubplot:>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 24,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:52.630Z",
          "iopub.execute_input": "2021-03-02T11:28:52.645Z",
          "iopub.status.idle": "2021-03-02T11:28:52.674Z",
          "shell.execute_reply": "2021-03-02T11:28:52.882Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.contry_of_res[df.asd_label == 1].value_counts().plot(kind='bar')\n"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 25,
          "data": {
            "text/plain": "<AxesSubplot:>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 25,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:52.690Z",
          "iopub.execute_input": "2021-03-02T11:28:52.701Z",
          "iopub.status.idle": "2021-03-02T11:28:52.730Z",
          "shell.execute_reply": "2021-03-02T11:28:52.891Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.contry_of_res[df.asd_label == 1].value_counts().head(10).plot(kind='bar')\n"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 26,
          "data": {
            "text/plain": "<AxesSubplot:>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 26,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:52.751Z",
          "iopub.execute_input": "2021-03-02T11:28:52.764Z",
          "iopub.status.idle": "2021-03-02T11:28:52.796Z",
          "shell.execute_reply": "2021-03-02T11:28:52.899Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "corr = df.corr()\n",
        "sns.heatmap(\n",
        "    data=corr,\n",
        "    annot=True,\n",
        "    fmt='.2f',\n",
        "    linewidths=.5,\n",
        "    cmap='RdYlGn',\n",
        "    xticklabels=corr.columns.values,\n",
        "    yticklabels=corr.columns.values\n",
        ")\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(40, 20)\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2880x1440 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 27,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:52.816Z",
          "iopub.execute_input": "2021-03-02T11:28:52.829Z",
          "iopub.status.idle": "2021-03-02T11:28:54.417Z",
          "shell.execute_reply": "2021-03-02T11:28:54.479Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(df['age'])"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "C:\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 28,
          "data": {
            "text/plain": "<AxesSubplot:xlabel='age', ylabel='Density'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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SQLWI1JrpfdvNYzO9zLU1mWBBU0L3QohIzVFAFGmml7kCnNXRrBaEiNQcBUSRZnqZKwQBcUCD1CJSYxQQRRpJZ2lqqKPOin+iXM7qxS30HBvGfd5NgCsiNUwBUaShdIaWxmKf0Ppqqxa3MJDK0Dc8VuKqRETio4Ao0nA6S8sMHxu6enELAPuPDZeyJBGRWCkgijScUkCIyPyigCjS8Fh2Fl1Mwb0QLykgRKSGKCCKNJzK0JqcWQuipTFBZ3uS/UcVECJSO2INCDO73Mx2m1m3mW2K2J40s3vC7dvMrCtcf5mZPW5mvwl/vzvOOosxlM7S3DCzFgQE3UzqYhKRWhJbQJhZPXArcAVwAXCVmV1QsNs1QJ+7rwVuBm4K1x8B/sDdXw9cDdwVV53FGknPvAUBCggRqT0z/yfx1C4GusPnR2BmdwMbgGfy9tkA3BAu3wvcYmbm7k/k7bMLaDazpLunYqx3UkMzuIpp87b9J5f7R8Z4+fgI33lkH598a1eJqxMRKb04u5hWAC/lve4J10Xu4+4ZoB9YUrDPR4B/qWQ4QHCj3EwHqQEWtzTiwHHdCyEiNSLOFsSsmdlrCbqd3jfB9muBawFWr14dWx3uHt4oN/MupkWtjQAcG5pXj9EQkRoWZwviALAq7/XKcF3kPmaWABYCR8PXK4EfAp909xejDuDut7n7endf39nZWeLyTxkdG8edWbUgOtuTAPQOVLQhJCJStDgDYjuwzszWmFkjsBHYUrDPFoJBaIArgQfd3c2sA/i/wCZ3fzjGGosymAom6mubxSB1a2M9zQ31HFZAiEiNiC0gwjGF64D7gWeB77n7LjO70cw+FO52O7DEzLqBzwO5S2GvA9YC15vZk+HPsrhqncpQGBCtyZm3IMyMZe1JtSBEpGbEOgbh7luBrQXrrs9bHgU+GvG+LwNfjrO26RjMC4jRsZmPIXS2J3n2YNQTXEVEqo/upC5CLiDaZ9GCAFjWnmQonaVPA9UiUgMUEEUoRRcTnBqo7u4dnHVNIiJxU0AUYbBkAdEEQPdhBYSIVD8FRBGGUsHzqNtmGRAdLQ001BsvKiBEpAYoIIpwqotp5pe5AtSZsbQtqS4mEakJCoginOximsWNcjnL2pM8f2hg1p8jIhI3BUQRBlMZWhvrqauzWX/WmQubebl/lH7NySQiVU4BUYShVGbWA9Q5yxcGA9XPHtL9ECJS3RQQRRhMZWY9QJ1zZhgQz+mGORGpcgqIIpSyBdGeTLC4tZFnD2ocQkSqmwKiCEOp7KyvYMoxM85f3q4uJhGpegqIIgykMrQlG0r2eeefuYDdhwbIjnvJPlNEpNQUEEUYSmVmNdV3ofOWLyCVGWfvkaGSfaaISKkpIIpQyjEIgPOXtwPwjAaqRaSKKSCKUMqrmADWLWunMVHHrgP9JftMEZFSU0BMIZMdJ5UZL2kLojFRx/nLF/B0jwJCRKqXAmIKpZqor9AbVixk54F+xjVQLSJVKtYnys0FA6lgSoxSBsTmbfsZSmUYSGW45cFulobPifj4JatLdgwRkdlSC2IKuRZEKbuYAFYsagag5/hIST9XRKRUFBBTGCzRVN+FlrU3kagzDvQNl/RzRURKRQExhdyzIEo9BlFfZ5zV0cwBtSBEpEopIKZQqudRR1nR0czLx0cZdw1Ui0j1UUBMYSCmFgQE4xDp7DhHBlIl/2wRkdlSQEwhri4mCFoQgLqZRKQqKSCmEGcXU2d7ksb6Ol3JJCJVSQExhcFUlsb6OhoTpT9VdWac1dHEgT4FhIhUHwXEFAZGx2hviu9+whUdzRzsH9HU3yJSdRQQUzg+PEZHS+meBVFoxaJmxrJOrwaqRaTKKCCmcHwkTUdLY2yfv6KjBYAe3TAnIlUm1oAws8vNbLeZdZvZpojtSTO7J9y+zcy6wvVLzOwXZjZoZrfEWeNU+obGWBRjC2JJWyPNDfXsP6aAEJHqEltAmFk9cCtwBXABcJWZXVCw2zVAn7uvBW4GbgrXjwL/FfhCXPUV6/hwmoXN8bUg6sxYvbhFASEiVSfOFsTFQLe773H3NHA3sKFgnw3AneHyvcB7zMzcfcjdf0UQFBV1fCTeFgTA2UtaODyQ4vhwOtbjiIhMR5wBsQJ4Ke91T7guch93zwD9wJJiD2Bm15rZDjPb0dvbO8tyT5fKZBlOZ2MdpAZYvTgYh3hi//FYjyMiMh01PUjt7re5+3p3X9/Z2Vnyz+8fDp4FEecgNcDKRS3UGez47bFYjyMiMh1xBsQBYFXe65Xhush9zCwBLASOxljTtPSdDIh4WxCNiTrO6mjm8d/2xXocEZHpiDMgtgPrzGyNmTUCG4EtBftsAa4Ol68EHnSvnqlNc2MCi2JuQQCcvbiFJ186TiqTjf1YIiLFiC0gwjGF64D7gWeB77n7LjO70cw+FO52O7DEzLqBzwMnL4U1s33AV4FPmVlPxBVQscu1IBY2x9uCADins43RsXG1IkSkasT6TGp33wpsLVh3fd7yKPDRCd7bFWdtxegfCVsQrfG3IM5Z2kp9nfFw9xHedu7S2I8nIjKVmh6kjtvJMYgytCCSDfW8cVUHv3rhSOzHEhEphgJiEseHx2isr6OlsbTPo57IpWuX8vSB/pNXT4mIVJICYhLHh9N0tDRgZmU53tvXLcUdfv2iWhEiUnkKiEnEPZNroQtXddDelOCBZw+X7ZgiIhNRQEyibzjemVwLNdTXcdn5Z/CzZw6RzoyX7bgiIlEUEJPoHxkrywB1vitev5wToxke2VM19wuKyDylgJhE33C6LDfJ5XvHuqW0Ntbzk50Hy3pcEZFCCohJlHsMAqCpoZ53n38G9+96hbGsuplEpHIUEBMYSWdJZcbLOgaxedt+Nm/bz6LmBo4NpfnSll1lO7aISCEFxASODAbPiF5ShruoC607o532pgTb92naDRGpHAXEBHr6RgBYsai57MeurzPetHoRz78ywKH+ij8zSUTmKQXEBHr6gkeArqxAQAC86exFOPC9HS9Nua+ISBwUEBPo6RvBDJYvrExALGlLsm5ZG3/36G91T4SIVIQCYgI9fSOcuaCJxkTlTtGla5dyeCDF1t/oklcRKT8FxAQOHB+uWPdSztplbZzb2codD++lip6jJCLzhAJiAj19I6xc1FLRGurM+JNL1/B0T78eJCQiZaeAiJDJjnOwf5QVHZVtQQD8q4tWsLC5gTse3lvpUkRknlFARDh0YpTsuFe8iwmgpTHBVRev5ic7D528skpEpBwUEBFy90BUuosp55NvPRsz41sP76t0KSIyjyggIpwKiMq3IADO6mjmQ797Fpu37efYULrS5YjIPKGAiHAgdw9ER1OlSznps+86l9FMljt+pbEIESmPRKULqEZ7jwxyRnsTyUR5nkU9mc3b9p9cfu3yBXzzl3voaGng0+84p4JVich8oBZEAXfn0T3HeNPZiypdymnefd4ZpDPj/OI5PZJUROKngCiw98gQh06M8ra1SypdymnOXNjE+q5FPLLnKHt6BytdjojMcQqIAg+/GDzq823nLq1wJdHee/4ZNNTX8VdbdjE+rrurRSQ+CogCv+4+wlkLm+haUh2XuBZqb2rg8tedyS9fOMK3fr2v0uWIyBymQeo84+POI3uO8t7zz8DMKl3OhC7uWsxwOstNP36O1561gLecU33dYSJS+9SCyLN150GOD4/xjnXV2b2UY2Z85SNv4OwlLfzrb29n+75jlS5JROagWAPCzC43s91m1m1mmyK2J83snnD7NjPrytv2xXD9bjN7f5x1AhwbSvNX/7iLN6xcyAdfvzzuw83aotZGvvvpSzhjQRNX3fYot/6im9GxbKXLEpE5JLYuJjOrB24FLgN6gO1mtsXdn8nb7Rqgz93XmtlG4CbgY2Z2AbAReC1wFvCAmb3G3Uv+DejuPPR8L39z/25OjI7x3SsvIVFf/Q2r3P0Rn7jkbO578gB/c/9uvv7PL/Lxi1dxyZolrFzczJLWJItbG6mvq97uMqkd05lyvthdp3OZxbSOX/RnTuP406h2Op87lh1nOJ1lKJVhOJ1lMJVhOJ1hKJVlJJ2lMVFHWzJBW1OC9qYE7ckG2poStCUTsT+vJs4xiIuBbnffA2BmdwMbgPyA2ADcEC7fC9xiQef/BuBud08Be82sO/y8R0pd5MPdR/nUt7azoqOZr218I+eduaDUh4hVc2M9G9+8ijd3Lebh7iN8+9f7+OYvX323dZ0FU4ebgRH+zlsuRjX8IRW9a4VrrfSXXvC509hZalZjfR11dfCB1y3nqx+7sOSfH2dArADyH6jcA1wy0T7unjGzfmBJuP7RgveuKDyAmV0LXBu+HDSz3dOscSlwBOC3wAe/OM13x+dkXVVItc1MtdZWrXWBaivabuDmjSdfTre2syfaUNNXMbn7bcBtM32/me1w9/UlLKkkqrUuUG0zVa21VWtdoNpmqpS1xdmBdQBYlfd6Zbguch8zSwALgaNFvldERGIUZ0BsB9aZ2RozayQYdN5SsM8W4Opw+UrgQQ86ZbcAG8OrnNYA64DHYqxVREQKxNbFFI4pXAfcD9QDd7j7LjO7Edjh7luA24G7wkHoYwQhQrjf9wgGtDPA5+K4golZdE/FrFrrAtU2U9VaW7XWBaptpkpWm03nKgoREZk/qv+CfxERqQgFhIiIRJqXATHVFCAVqGefmf3GzJ40sx3husVm9jMzeyH8XZYnGJnZHWZ22Mx25q2LrMUC/ys8j0+b2UUVqO0GMzsQnrsnzewDedvKMl2Lma0ys1+Y2TNmtsvM/kO4vuLnbZLaquG8NZnZY2b2VFjbl8L1a8Kpd7rDqXgaw/UTTs1Tprq+bWZ7887ZheH6sv4dhMesN7MnzOxH4et4zpm7z6sfggHzF4FzgEbgKeCCCte0D1hasO4rwKZweRNwU5lqeSdwEbBzqlqADwA/Bgx4C7CtArXdAHwhYt8Lwv+3SWBN+P+8Pqa6lgMXhcvtwPPh8St+3iaprRrOmwFt4XIDsC08H98DNobrvwH8u3D5s8A3wuWNwD1lruvbwJUR+5f17yA85ueBzcCPwtexnLP52II4OQWIu6eB3BQg1WYDcGe4fCfwh+U4qLv/P4IryoqpZQPwHQ88CnSYWWwzHU5Q20ROTtfi7nuB3HQtcdR10N3/JVweAJ4luPO/4udtktomUs7z5u6eezRiQ/jjwLsJpt6B089b7nzeC7zHrPTz8k9S10TK+ndgZiuBDwL/J3xtxHTO5mNARE0BMtkfTDk48FMze9yC6UMAznD3g+HyIeCMypQ2aS3Vci6vC5v2d+R1xVWktrAJ/0aCf3VW1XkrqA2q4LyFXSVPAoeBnxG0WI67eybi+K+amgfITc0Te13unjtn/y08ZzebWbKwroia4/A/gf8MjIevlxDTOZuPAVGN3u7uFwFXAJ8zs3fmb/SgfVgV1yNXUy2h/w2cC1wIHAT+R6UKMbM24AfAf3T3E/nbKn3eImqrivPm7ll3v5BgtoSLgfMqUUehwrrM7HXAFwnqezOwGPjzctdlZr8PHHb3x8txvPkYEFU3jYe7Hwh/HwZ+SPCH8kqumRr+Ply5CiespeLn0t1fCf+Yx4Fvcqo7pKy1mVkDwRfwd939H8LVVXHeomqrlvOW4+7HgV8AbyXoosndxJt//Imm5ilHXZeH3XXuwSzT36Iy5+xS4ENmto+ge/zdwNeI6ZzNx4AoZgqQsjGzVjNrzy0D7wN28uppSK4G/rEyFcIktWwBPhlexfEWoD+vS6UsCvp6P0xw7nK1lWW6lrBP93bgWXf/at6mip+3iWqrkvPWaWYd4XIzwbNjniX4Qr4y3K3wvEVNzVOOup7LC3sj6OPPP2dl+f/p7l9095Xu3kXw3fWgu/8RcZ2zOEbYq/2H4KqD5wn6O/+ywrWcQ3DVyFPArlw9BP2EPwdeAB4AFpepnr8n6HIYI+jLvGaiWgiu2rg1PI+/AdZXoLa7wmM/Hf4xLM/b/y/D2nYDV8RY19sJuo+eBp4Mfz5QDedtktqq4by9AXgirGEncH3e38RjBAPk3weS4fqm8HV3uP2cMtf1YHjOdgJ/x6krncr6d5BX57s4dRVTLOdMU22IiEik+djFJCIiRVBAiIhIJAWEiIhEUkCIiEgkBYSIiERSQIiISCQFhIiIRFJAiJSAmd0XTra4KzfhopldY2bPh88W+KaZ3RKu7zSzH5jZ9vDn0spWLxJNN8qJlICZLXb3Y+HUDNuB9wMPEzy/YoDgLtyn3P06M9sMfN3df2Vmq4H73f38ihUvMoHE1LuISBH+1Mw+HC6vAv4YeMjdjwGY2feB14Tb3wtckDct/wIza/NTzyAQqQoKCJFZMrN3EXzpv9Xdh83sn4HngIlaBXXAW9x9tCwFisyQxiBEZm8h0BeGw3kEj51sBX7PzBaF0yx/JG//nwL/PvfCwmcbi1QbBYTI7P0ESJjZs8BfA48SzMP/3wlm0HyY4Lnj/eH+fwqsD59M9gzwmbJXLFIEDVKLxCQ3rhC2IH4I3OHuP6x0XSLFUgtCJD43hM813gnsBe6raDUi06QWhIiIRFILQkREIikgREQkkgJCREQiKSBERCSSAkJERCL9fzeXPTJBLdieAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 28,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:54.436Z",
          "iopub.execute_input": "2021-03-02T11:28:54.447Z",
          "iopub.status.idle": "2021-03-02T11:28:54.634Z",
          "shell.execute_reply": "2021-03-02T11:28:54.680Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.asd_label.hist()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 29,
          "data": {
            "text/plain": "<AxesSubplot:>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 29,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:54.651Z",
          "iopub.execute_input": "2021-03-02T11:28:54.662Z",
          "iopub.status.idle": "2021-03-02T11:28:54.727Z",
          "shell.execute_reply": "2021-03-02T11:28:54.780Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (30, 20))\n",
        "sns.countplot(x = 'asd_label', hue = 'contry_of_res', data = df)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 30,
          "data": {
            "text/plain": "<AxesSubplot:xlabel='asd_label', ylabel='count'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2160x1440 with 1 Axes>",
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 30,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:54.743Z",
          "iopub.execute_input": "2021-03-02T11:28:54.754Z",
          "iopub.status.idle": "2021-03-02T11:28:56.146Z",
          "shell.execute_reply": "2021-03-02T11:28:56.188Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (21, 21))\n",
        "sns.countplot(x = 'asd_label', hue = 'ethnicity', data = df)\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1512x1512 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 31,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:56.165Z",
          "iopub.execute_input": "2021-03-02T11:28:56.176Z",
          "iopub.status.idle": "2021-03-02T11:28:56.408Z",
          "shell.execute_reply": "2021-03-02T11:28:56.391Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='asd_label',hue=\"juandice\",data=df)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 32,
          "data": {
            "text/plain": "<AxesSubplot:xlabel='asd_label', ylabel='count'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 32,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:56.426Z",
          "iopub.execute_input": "2021-03-02T11:28:56.439Z",
          "iopub.status.idle": "2021-03-02T11:28:56.499Z",
          "shell.execute_reply": "2021-03-02T11:28:56.568Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='asd_label',hue=\"relative_autism\",data=df)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 33,
          "data": {
            "text/plain": "<AxesSubplot:xlabel='asd_label', ylabel='count'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 33,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:56.520Z",
          "iopub.execute_input": "2021-03-02T11:28:56.531Z",
          "iopub.status.idle": "2021-03-02T11:28:56.614Z",
          "shell.execute_reply": "2021-03-02T11:28:56.778Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.age.max()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 34,
          "data": {
            "text/plain": "383.0"
          },
          "metadata": {}
        }
      ],
      "execution_count": 34,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:28:56.630Z",
          "iopub.execute_input": "2021-03-02T11:28:56.643Z",
          "shell.execute_reply": "2021-03-02T11:28:56.789Z",
          "iopub.status.idle": "2021-03-02T11:28:56.667Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.age.replace(df.age.max(), df.age.median(), inplace=True)\n"
      ],
      "outputs": [],
      "execution_count": 11,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:03:00.663Z",
          "iopub.execute_input": "2021-04-25T12:03:00.695Z",
          "iopub.status.idle": "2021-04-25T12:03:00.739Z",
          "shell.execute_reply": "2021-04-25T12:03:00.772Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mapping = {}\n",
        "\n",
        "it = 0\n",
        "for (columnName, columnData) in df.iteritems():\n",
        "    # print('Colunm Name : ', columnName)\n",
        "    for i in range(len(df[columnName])):\n",
        "        if isinstance(df[columnName][i], str):\n",
        "            if df[columnName][i] not in mapping:\n",
        "                mapping[df[columnName][i]] = it\n",
        "                it += 1\n",
        "            df[columnName][i] = mapping[df[columnName][i]]\n",
        "print(df)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  A7_Score  \\\n",
            "0           1         1         1         1         0         0         1   \n",
            "1           1         1         0         1         0         0         0   \n",
            "2           1         1         0         1         1         0         1   \n",
            "3           1         1         0         1         0         0         1   \n",
            "4           1         0         0         0         0         0         0   \n",
            "..        ...       ...       ...       ...       ...       ...       ...   \n",
            "699         0         1         0         1         1         0         1   \n",
            "700         1         0         0         0         0         0         0   \n",
            "701         1         0         1         1         1         0         1   \n",
            "702         1         0         0         1         1         0         1   \n",
            "703         1         0         1         1         1         0         1   \n",
            "\n",
            "     A8_Score  A9_Score  A10_Score   age  gender ethnicity  juandice  \\\n",
            "0           1         0          0  26.0       1         0         0   \n",
            "1           1         0          1  24.0       0         1         0   \n",
            "2           1         1          1  27.0       0         1         1   \n",
            "3           1         0          1  35.0       1         0         0   \n",
            "4           1         0          0  40.0       1       NaN         0   \n",
            "..        ...       ...        ...   ...     ...       ...       ...   \n",
            "699         1         1          1  25.0       1         0         0   \n",
            "700         1         0          1  34.0       0         8         0   \n",
            "701         1         0          1  24.0       1       NaN         0   \n",
            "702         0         1          1  35.0       0         7         0   \n",
            "703         1         1          1  26.0       1         0         0   \n",
            "\n",
            "     relative_autism contry_of_res  asd_label  \n",
            "0                  0            11          0  \n",
            "1                  1            12          0  \n",
            "2                  1            13          1  \n",
            "3                  1            11          0  \n",
            "4                  0            14          0  \n",
            "..               ...           ...        ...  \n",
            "699                0            51          1  \n",
            "700                0            50          0  \n",
            "701                0            51          1  \n",
            "702                0            28          0  \n",
            "703                0            77          1  \n",
            "\n",
            "[704 rows x 17 columns]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-12-66f2a147c0b3>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[columnName][i] = mapping[df[columnName][i]]\n"
          ]
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:03:01.688Z",
          "iopub.execute_input": "2021-04-25T12:03:01.712Z",
          "iopub.status.idle": "2021-04-25T12:03:01.897Z",
          "shell.execute_reply": "2021-04-25T12:03:01.969Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.contry_of_res.replace(np.nan, df.contry_of_res.median(), inplace=True)\n",
        "df.age.replace(np.nan, df.age.median(), inplace=True)\n"
      ],
      "outputs": [],
      "execution_count": 13,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:03:05.437Z",
          "iopub.execute_input": "2021-04-25T12:03:05.471Z",
          "iopub.status.idle": "2021-04-25T12:03:05.521Z",
          "shell.execute_reply": "2021-04-25T12:03:05.550Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.ethnicity.replace(np.nan, df.ethnicity.median(), inplace=True)\n"
      ],
      "outputs": [],
      "execution_count": 14,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:03:06.342Z",
          "iopub.execute_input": "2021-04-25T12:03:06.366Z",
          "iopub.status.idle": "2021-04-25T12:03:06.397Z",
          "shell.execute_reply": "2021-04-25T12:03:06.426Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.utils import shuffle\n",
        "from sklearn.model_selection import train_test_split\n",
        "X = df.drop(columns = ['asd_label'])\n",
        "Y = df['asd_label']\n"
      ],
      "outputs": [],
      "execution_count": 15,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:03:09.123Z",
          "iopub.execute_input": "2021-04-25T12:03:09.144Z",
          "iopub.status.idle": "2021-04-25T12:03:10.969Z",
          "shell.execute_reply": "2021-04-25T12:03:11.069Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X, Y = shuffle(X, Y)\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3)\n"
      ],
      "outputs": [],
      "execution_count": 16,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:03:11.005Z",
          "iopub.execute_input": "2021-04-25T12:03:11.022Z",
          "iopub.status.idle": "2021-04-25T12:03:11.049Z",
          "shell.execute_reply": "2021-04-25T12:03:11.085Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "model_log_reg = LogisticRegression(\n",
        "    C=1,\n",
        "    penalty='l1',\n",
        "    solver='liblinear',\n",
        "    random_state=42,\n",
        "    multi_class='ovr'\n",
        ")"
      ],
      "outputs": [],
      "execution_count": 17,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:03:15.243Z",
          "iopub.execute_input": "2021-04-25T12:03:15.263Z",
          "iopub.status.idle": "2021-04-25T12:03:15.454Z",
          "shell.execute_reply": "2021-04-25T12:03:15.491Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for col in df.columns:\n",
        "  print(col)\n",
        "  print(set(df[col].values))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A1_Score\n",
            "{0, 1}\n",
            "A2_Score\n",
            "{0, 1}\n",
            "A3_Score\n",
            "{0, 1}\n",
            "A4_Score\n",
            "{0, 1}\n",
            "A5_Score\n",
            "{0, 1}\n",
            "A6_Score\n",
            "{0, 1}\n",
            "A7_Score\n",
            "{0, 1}\n",
            "A8_Score\n",
            "{0, 1}\n",
            "A9_Score\n",
            "{0, 1}\n",
            "A10_Score\n",
            "{0, 1}\n",
            "age\n",
            "{17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 58.0, 59.0, 60.0, 61.0, 64.0}\n",
            "gender\n",
            "{0, 1}\n",
            "ethnicity\n",
            "{0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0}\n",
            "juandice\n",
            "{0, 1}\n",
            "relative_autism\n",
            "{0, 1}\n",
            "contry_of_res\n",
            "{11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77}\n",
            "asd_label\n",
            "{0, 1}\n"
          ]
        }
      ],
      "execution_count": 18,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": true
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:03:17.912Z",
          "iopub.execute_input": "2021-04-25T12:03:17.929Z",
          "iopub.status.idle": "2021-04-25T12:03:17.970Z",
          "shell.execute_reply": "2021-04-25T12:03:17.988Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_log_reg.fit(X_train, Y_train)\n"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 19,
          "data": {
            "text/plain": "LogisticRegression(C=1, multi_class='ovr', penalty='l1', random_state=42,\n                   solver='liblinear')"
          },
          "metadata": {}
        }
      ],
      "execution_count": 19,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:03:21.231Z",
          "iopub.execute_input": "2021-04-25T12:03:21.256Z",
          "iopub.status.idle": "2021-04-25T12:03:21.413Z",
          "shell.execute_reply": "2021-04-25T12:03:21.430Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "type(X_test)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 35,
          "data": {
            "text/plain": "pandas.core.frame.DataFrame"
          },
          "metadata": {}
        }
      ],
      "execution_count": 35,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:21:37.011Z",
          "iopub.execute_input": "2021-04-25T12:21:37.045Z",
          "iopub.status.idle": "2021-04-25T12:21:37.092Z",
          "shell.execute_reply": "2021-04-25T12:21:37.112Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_log_reg_predict = model_log_reg.predict(X_test)\n"
      ],
      "outputs": [],
      "execution_count": 20,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:03:28.097Z",
          "iopub.execute_input": "2021-04-25T12:03:28.127Z",
          "iopub.status.idle": "2021-04-25T12:03:28.163Z",
          "shell.execute_reply": "2021-04-25T12:03:28.179Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Logistic Regression Score:', model_log_reg.score(X_test, Y_test))\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression Score: 0.9905660377358491\n"
          ]
        }
      ],
      "execution_count": 21,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:03:29.627Z",
          "iopub.execute_input": "2021-04-25T12:03:29.660Z",
          "iopub.status.idle": "2021-04-25T12:03:29.727Z",
          "shell.execute_reply": "2021-04-25T12:03:29.757Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle"
      ],
      "outputs": [],
      "execution_count": 23,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:08:30.796Z",
          "iopub.execute_input": "2021-04-25T12:08:30.829Z",
          "iopub.status.idle": "2021-04-25T12:08:30.879Z",
          "shell.execute_reply": "2021-04-25T12:08:30.911Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pickle.dump(model_log_reg, open('log_Reg_model.pkl', 'wb'))"
      ],
      "outputs": [],
      "execution_count": 28,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:12:54.111Z",
          "iopub.execute_input": "2021-04-25T12:12:54.142Z",
          "iopub.status.idle": "2021-04-25T12:12:54.191Z",
          "shell.execute_reply": "2021-04-25T12:12:54.211Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import pickle\n",
        "model = pickle.load(open('log_Reg_model.pkl', 'rb'))\n",
        "testcase = {'A1_Score': [1],\n",
        "          'A2_Score': [0],\n",
        "          'A3_Score': [1],\n",
        "          'A4_Score': [1],\n",
        "          'A5_Score': [1],\n",
        "          'A6_Score': [1],\n",
        "          'A7_Score': [0],\n",
        "          'A8_Score': [1],\n",
        "          'A9_Score': [0.75],\n",
        "          'A10_Score': [0.75],\n",
        "          'age': [21.0],\n",
        "          'gender': [0],\n",
        "          'ethnicity': [3],\n",
        "          'juandice': [0],\n",
        "          'relative_autism': [0],\n",
        "          'country_of_res': [41]}\n",
        "ip_df = pd.DataFrame(data=testcase)\n",
        "ans = model.predict_proba(ip_df)\n",
        "print(ans)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.08973519 0.91026481]]\n"
          ]
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:38:59.736Z",
          "iopub.execute_input": "2021-04-25T12:38:59.769Z",
          "iopub.status.idle": "2021-04-25T12:38:59.813Z",
          "shell.execute_reply": "2021-04-25T12:38:59.832Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model_lin_reg = LinearRegression()\n",
        "model_lin_reg.fit(X_train, Y_train)\n",
        "print('Linear Regression Score:', model_lin_reg.score(X_test, Y_test))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linear Regression Score: 0.6949698768958248\n"
          ]
        }
      ],
      "execution_count": 58,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:31:55.491Z",
          "iopub.execute_input": "2021-03-02T11:31:55.506Z",
          "iopub.status.idle": "2021-03-02T11:31:55.534Z",
          "shell.execute_reply": "2021-03-02T11:31:55.547Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "tree_clf = DecisionTreeRegressor()\n",
        "tree_clf.fit(X_train, Y_train)\n",
        "print(\"Decision Tree Score: \", tree_clf.score(X_test, Y_test))\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Score:  0.3902205177372964\n"
          ]
        }
      ],
      "execution_count": 59,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:31:58.361Z",
          "iopub.execute_input": "2021-03-02T11:31:58.373Z",
          "iopub.status.idle": "2021-03-02T11:31:58.406Z",
          "shell.execute_reply": "2021-03-02T11:31:58.418Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Best Model seems to be Log Regression So analyze its other performence measures:\n",
        "from sklearn.metrics import accuracy_score, roc_auc_score, classification_report, roc_curve\n",
        "print('\\nLogistic Regression Accuracy: {:.2f}%'.format(accuracy_score(Y_test, model_log_reg_predict) * 100))\n",
        "print('Logistic Regression AUC: {:.2f}%'.format(roc_auc_score(Y_test, model_log_reg_predict) * 100))\n",
        "print('Logistic Regression Classification report:\\n\\n', classification_report(Y_test, model_log_reg_predict))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Logistic Regression Accuracy: 99.06%\n",
            "Logistic Regression AUC: 98.00%\n",
            "Logistic Regression Classification report:\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      1.00      0.99       162\n",
            "           1       1.00      0.96      0.98        50\n",
            "\n",
            "    accuracy                           0.99       212\n",
            "   macro avg       0.99      0.98      0.99       212\n",
            "weighted avg       0.99      0.99      0.99       212\n",
            "\n"
          ]
        }
      ],
      "execution_count": 22,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-04-25T12:05:19.515Z",
          "iopub.execute_input": "2021-04-25T12:05:19.550Z",
          "iopub.status.idle": "2021-04-25T12:05:19.626Z",
          "shell.execute_reply": "2021-04-25T12:05:19.661Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Applying some other models to see if anyone performs better than Logistic Regression:"
      ],
      "outputs": [],
      "execution_count": 63,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2021-03-02T11:34:40.292Z",
          "iopub.status.busy": "2021-03-02T11:34:40.252Z",
          "iopub.execute_input": "2021-03-02T11:34:40.263Z",
          "iopub.status.idle": "2021-03-02T11:34:40.281Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import SGDClassifier\n",
        "sgd_clf = SGDClassifier(random_state = 42)\n",
        "sgd_clf.fit(X_train, Y_train)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 64,
          "data": {
            "text/plain": "SGDClassifier(random_state=42)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 64,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:36:36.927Z",
          "iopub.execute_input": "2021-03-02T11:36:36.942Z",
          "iopub.status.idle": "2021-03-02T11:36:36.971Z",
          "shell.execute_reply": "2021-03-02T11:36:36.985Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Stochastic Gradient Descent Classifier Score: \", sgd_clf.score(X_test, Y_test))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stochastic Gradient Descent Classifier Score:  0.8537735849056604\n"
          ]
        }
      ],
      "execution_count": 65,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:37:56.599Z",
          "iopub.execute_input": "2021-03-02T11:37:56.614Z",
          "iopub.status.idle": "2021-03-02T11:37:56.640Z",
          "shell.execute_reply": "2021-03-02T11:37:56.654Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "svm_clf = SVC()\n",
        "svm_clf.fit(X_train, Y_train)\n"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 66,
          "data": {
            "text/plain": "SVC()"
          },
          "metadata": {}
        }
      ],
      "execution_count": 66,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:39:42.026Z",
          "iopub.execute_input": "2021-03-02T11:39:42.044Z",
          "iopub.status.idle": "2021-03-02T11:39:42.128Z",
          "shell.execute_reply": "2021-03-02T11:39:42.141Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Support Vector Machine Classifier Score: \", svm_clf.score(X_test, Y_test))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Support Vector Machine Classifier Score:  0.7028301886792453\n"
          ]
        }
      ],
      "execution_count": 67,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-03-02T11:40:23.513Z",
          "iopub.execute_input": "2021-03-02T11:40:23.527Z",
          "iopub.status.idle": "2021-03-02T11:40:23.552Z",
          "shell.execute_reply": "2021-03-02T11:40:23.565Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn_clf = KNeighborsClassifier()\n",
        "knn_clf.fit(X_train, Y_train)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 71,
          "data": {
            "text/plain": "KNeighborsClassifier()"
          },
          "metadata": {}
        }
      ],
      "execution_count": 71,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
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        "execution": {
          "iopub.status.busy": "2021-03-02T11:42:39.450Z",
          "iopub.execute_input": "2021-03-02T11:42:39.464Z",
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          "shell.execute_reply": "2021-03-02T11:42:39.505Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"KNN Classifier Score: \", knn_clf.score(X_test, Y_test))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN Classifier Score:  0.7641509433962265\n"
          ]
        }
      ],
      "execution_count": 72,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
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        "execution": {
          "iopub.status.busy": "2021-03-02T11:43:05.165Z",
          "iopub.execute_input": "2021-03-02T11:43:05.177Z",
          "iopub.status.idle": "2021-03-02T11:43:05.207Z",
          "shell.execute_reply": "2021-03-02T11:43:05.220Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "for k in range (1, 100):\n",
        "  knn_reg  = KNeighborsRegressor(n_neighbors=k)\n",
        "  knn_reg.fit(X_train, Y_train)\n",
        "  print(\"k: \", k, \"KNN Regressor Score: \", knn_reg.score(X_test, Y_test))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "k:  1 KNN Regressor Score:  0.05145413870246107\n",
            "k:  2 KNN Regressor Score:  0.1304996271439226\n",
            "k:  3 KNN Regressor Score:  0.2220920185126004\n",
            "k:  4 KNN Regressor Score:  0.26036007244060955\n",
            "k:  5 KNN Regressor Score:  0.23754980291893046\n",
            "k:  6 KNN Regressor Score:  0.19699821265816808\n",
            "k:  7 KNN Regressor Score:  0.17128986461954565\n",
            "k:  8 KNN Regressor Score:  0.143203366357729\n",
            "k:  9 KNN Regressor Score:  0.14513768055900822\n",
            "k:  10 KNN Regressor Score:  0.1454053478214553\n",
            "k:  11 KNN Regressor Score:  0.1311528956434388\n",
            "k:  12 KNN Regressor Score:  0.11669803392398481\n",
            "k:  13 KNN Regressor Score:  0.11613379450240602\n",
            "k:  14 KNN Regressor Score:  0.11966832114757064\n",
            "k:  15 KNN Regressor Score:  0.11639690825373172\n",
            "k:  16 KNN Regressor Score:  0.10641545488441484\n",
            "k:  17 KNN Regressor Score:  0.10857944967696276\n",
            "k:  18 KNN Regressor Score:  0.09320744344358567\n",
            "k:  19 KNN Regressor Score:  0.08967874767573603\n",
            "k:  20 KNN Regressor Score:  0.09075103867050205\n",
            "k:  21 KNN Regressor Score:  0.08084973984622457\n",
            "k:  22 KNN Regressor Score:  0.07777152682582844\n",
            "k:  23 KNN Regressor Score:  0.07489241747878417\n",
            "k:  24 KNN Regressor Score:  0.06956872980362927\n",
            "k:  25 KNN Regressor Score:  0.0664501970810698\n",
            "k:  26 KNN Regressor Score:  0.06912745374283846\n",
            "k:  27 KNN Regressor Score:  0.072210743545016\n",
            "k:  28 KNN Regressor Score:  0.07066818418003218\n",
            "k:  29 KNN Regressor Score:  0.0672981469173285\n",
            "k:  30 KNN Regressor Score:  0.06553176378679759\n",
            "k:  31 KNN Regressor Score:  0.06496719232334414\n",
            "k:  32 KNN Regressor Score:  0.0679513555981679\n",
            "k:  33 KNN Regressor Score:  0.06922718962580687\n",
            "k:  34 KNN Regressor Score:  0.06628249404775743\n",
            "k:  35 KNN Regressor Score:  0.06199968258316457\n",
            "k:  36 KNN Regressor Score:  0.055305340850953844\n",
            "k:  37 KNN Regressor Score:  0.05199854048031105\n",
            "k:  38 KNN Regressor Score:  0.055145222056672494\n",
            "k:  39 KNN Regressor Score:  0.05265685957477417\n",
            "k:  40 KNN Regressor Score:  0.05077660594439137\n",
            "k:  41 KNN Regressor Score:  0.052394596625619516\n",
            "k:  42 KNN Regressor Score:  0.04719075229964176\n",
            "k:  43 KNN Regressor Score:  0.04791196275437737\n",
            "k:  44 KNN Regressor Score:  0.047067907348566695\n",
            "k:  45 KNN Regressor Score:  0.050929956980168556\n",
            "k:  46 KNN Regressor Score:  0.0523400117163203\n",
            "k:  47 KNN Regressor Score:  0.05709768906392865\n",
            "k:  48 KNN Regressor Score:  0.05780600830936422\n",
            "k:  49 KNN Regressor Score:  0.05602558005220237\n",
            "k:  50 KNN Regressor Score:  0.05509474805582226\n",
            "k:  51 KNN Regressor Score:  0.05394615333229569\n",
            "k:  52 KNN Regressor Score:  0.05644877121387193\n",
            "k:  53 KNN Regressor Score:  0.05789419731423029\n",
            "k:  54 KNN Regressor Score:  0.055179484571591275\n",
            "k:  55 KNN Regressor Score:  0.05520203340825702\n",
            "k:  56 KNN Regressor Score:  0.05437801518817831\n",
            "k:  57 KNN Regressor Score:  0.053699373963120856\n",
            "k:  58 KNN Regressor Score:  0.05580452739874675\n",
            "k:  59 KNN Regressor Score:  0.050708028703629004\n",
            "k:  60 KNN Regressor Score:  0.05171762366393262\n",
            "k:  61 KNN Regressor Score:  0.05130240244024009\n",
            "k:  62 KNN Regressor Score:  0.04825800775908706\n",
            "k:  63 KNN Regressor Score:  0.04807415668941506\n",
            "k:  64 KNN Regressor Score:  0.04823960746910638\n",
            "k:  65 KNN Regressor Score:  0.045713151071953395\n",
            "k:  66 KNN Regressor Score:  0.04490589969540559\n",
            "k:  67 KNN Regressor Score:  0.04782674493571104\n",
            "k:  68 KNN Regressor Score:  0.050848501000611\n",
            "k:  69 KNN Regressor Score:  0.0556902008249579\n",
            "k:  70 KNN Regressor Score:  0.05382780788889563\n",
            "k:  71 KNN Regressor Score:  0.052184402800625196\n",
            "k:  72 KNN Regressor Score:  0.05281338651957623\n",
            "k:  73 KNN Regressor Score:  0.053547722129535735\n",
            "k:  74 KNN Regressor Score:  0.05158199063513802\n",
            "k:  75 KNN Regressor Score:  0.048262206597777246\n",
            "k:  76 KNN Regressor Score:  0.04522543554222935\n",
            "k:  77 KNN Regressor Score:  0.042194105336140875\n",
            "k:  78 KNN Regressor Score:  0.041546539911709646\n",
            "k:  79 KNN Regressor Score:  0.041072033896063176\n",
            "k:  80 KNN Regressor Score:  0.041319377863002105\n",
            "k:  81 KNN Regressor Score:  0.038707586839777375\n",
            "k:  82 KNN Regressor Score:  0.0364907813893518\n",
            "k:  83 KNN Regressor Score:  0.03552472625043357\n",
            "k:  84 KNN Regressor Score:  0.03302427465784108\n",
            "k:  85 KNN Regressor Score:  0.034121178409513786\n",
            "k:  86 KNN Regressor Score:  0.032207298184554256\n",
            "k:  87 KNN Regressor Score:  0.03251890779256483\n",
            "k:  88 KNN Regressor Score:  0.030960755907369908\n",
            "k:  89 KNN Regressor Score:  0.03156695700176693\n",
            "k:  90 KNN Regressor Score:  0.03418123567266029\n",
            "k:  91 KNN Regressor Score:  0.03488604505325088\n",
            "k:  92 KNN Regressor Score:  0.03475329775744651\n",
            "k:  93 KNN Regressor Score:  0.031094471246996935\n",
            "k:  94 KNN Regressor Score:  0.029802637293044443\n",
            "k:  95 KNN Regressor Score:  0.028727074958089016\n",
            "k:  96 KNN Regressor Score:  0.027068546704662633\n",
            "k:  97 KNN Regressor Score:  0.02296257446153216\n",
            "k:  98 KNN Regressor Score:  0.021365871176772466\n",
            "k:  99 KNN Regressor Score:  0.019604130941215603\n"
          ]
        }
      ],
      "execution_count": 83,
      "metadata": {
        "collapsed": true,
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        "execution": {
          "iopub.status.busy": "2021-03-02T11:47:27.815Z",
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          "shell.execute_reply": "2021-03-02T11:47:28.541Z"
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    },
    {
      "cell_type": "code",
      "source": [
        "print(\"K = 4 gave the optimum score\")"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "K = 4 gave the optimum score\n"
          ]
        }
      ],
      "execution_count": 84,
      "metadata": {
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        "jupyter": {
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        "execution": {
          "iopub.status.busy": "2021-03-02T11:48:16.943Z",
          "iopub.execute_input": "2021-03-02T11:48:16.956Z",
          "iopub.status.idle": "2021-03-02T11:48:16.987Z",
          "shell.execute_reply": "2021-03-02T11:48:16.999Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"It is clearly evident Logistic Regression gave the maximum score of 98.5%. Hence I propose to go forward with Log Reg Model for our apps QnA component.\")"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "It is clearly evident Logistic Regression gave the maximum score of 98.5%. Hence I propose to go forward with Log Reg Model for our apps QnA component.\n"
          ]
        }
      ],
      "execution_count": 85,
      "metadata": {
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