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{
"cells": [
{
"cell_type": "markdown",
"source": [
"# **Importing Relevant Libraries**"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 1,
"source": [
"import numpy as np\r\n",
"import matplotlib.pyplot as plt\r\n",
"import seaborn as sns\r\n",
"import pandas as pd\r\n",
"import warnings\r\n",
"%matplotlib inline\r\n",
"warnings.filterwarnings('ignore')"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"# **Preprocessing**"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Putting the dataset into a data frame "
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 2,
"source": [
"df = pd.read_csv('Asteroid_Updated.csv')\r\n",
"df"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" name a e i om w \\\n",
"0 Ceres 2.769165 0.076009 10.594067 80.305532 73.597694 \n",
"1 Pallas 2.772466 0.230337 34.836234 173.080063 310.048857 \n",
"2 Juno 2.669150 0.256942 12.988919 169.852760 248.138626 \n",
"3 Vesta 2.361418 0.088721 7.141771 103.810804 150.728541 \n",
"4 Astraea 2.574249 0.191095 5.366988 141.576605 358.687607 \n",
"... ... ... ... ... ... ... \n",
"839709 NaN 2.812945 0.664688 4.695700 183.310012 234.618352 \n",
"839710 NaN 2.645238 0.259376 12.574937 1.620020 339.568072 \n",
"839711 NaN 2.373137 0.202053 0.732484 176.499082 198.026527 \n",
"839712 NaN 2.260404 0.258348 9.661947 204.512448 148.496988 \n",
"839713 NaN 2.546442 0.287672 5.356238 70.709555 273.483265 \n",
"\n",
" q ad per_y data_arc ... UB IR spec_B spec_T \\\n",
"0 2.558684 2.979647 4.608202 8822.0 ... 0.426 NaN C G \n",
"1 2.133865 3.411067 4.616444 72318.0 ... 0.284 NaN B B \n",
"2 1.983332 3.354967 4.360814 72684.0 ... 0.433 NaN Sk S \n",
"3 2.151909 2.570926 3.628837 24288.0 ... 0.492 NaN V V \n",
"4 2.082324 3.066174 4.130323 63507.0 ... 0.411 NaN S S \n",
"... ... ... ... ... ... ... .. ... ... \n",
"839709 0.943214 4.682676 4.717914 17298.0 ... NaN NaN NaN NaN \n",
"839710 1.959126 3.331350 4.302346 16.0 ... NaN NaN NaN NaN \n",
"839711 1.893638 2.852636 3.655884 5.0 ... NaN NaN NaN NaN \n",
"839712 1.676433 2.844376 3.398501 10.0 ... NaN NaN NaN NaN \n",
"839713 1.813901 3.278983 4.063580 11.0 ... NaN NaN NaN NaN \n",
"\n",
" G moid class n per ma \n",
"0 0.12 1.594780 MBA 0.213885 1683.145708 77.372096 \n",
"1 0.11 1.233240 MBA 0.213503 1686.155999 59.699133 \n",
"2 0.32 1.034540 MBA 0.226019 1592.787285 34.925016 \n",
"3 0.32 1.139480 MBA 0.271609 1325.432765 95.861936 \n",
"4 NaN 1.095890 MBA 0.238632 1508.600458 282.366289 \n",
"... ... ... ... ... ... ... \n",
"839709 NaN 0.032397 APO 0.208911 1723.217927 156.905910 \n",
"839710 NaN 0.956145 MBA 0.229090 1571.431965 13.366251 \n",
"839711 NaN 0.893896 MBA 0.269600 1335.311579 355.351127 \n",
"839712 NaN 0.680220 MBA 0.290018 1241.302609 15.320134 \n",
"839713 NaN 0.815280 MBA 0.242551 1484.222588 20.432959 \n",
"\n",
"[839714 rows x 31 columns]"
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"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" <th>e</th>\n",
" <th>i</th>\n",
" <th>om</th>\n",
" <th>w</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Ceres</td>\n",
" <td>2.769165</td>\n",
" <td>0.076009</td>\n",
" <td>10.594067</td>\n",
" <td>80.305532</td>\n",
" <td>73.597694</td>\n",
" <td>2.558684</td>\n",
" <td>2.979647</td>\n",
" <td>4.608202</td>\n",
" <td>8822.0</td>\n",
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" <td>0.426</td>\n",
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" <td>G</td>\n",
" <td>0.12</td>\n",
" <td>1.594780</td>\n",
" <td>MBA</td>\n",
" <td>0.213885</td>\n",
" <td>1683.145708</td>\n",
" <td>77.372096</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Pallas</td>\n",
" <td>2.772466</td>\n",
" <td>0.230337</td>\n",
" <td>34.836234</td>\n",
" <td>173.080063</td>\n",
" <td>310.048857</td>\n",
" <td>2.133865</td>\n",
" <td>3.411067</td>\n",
" <td>4.616444</td>\n",
" <td>72318.0</td>\n",
" <td>...</td>\n",
" <td>0.284</td>\n",
" <td>NaN</td>\n",
" <td>B</td>\n",
" <td>B</td>\n",
" <td>0.11</td>\n",
" <td>1.233240</td>\n",
" <td>MBA</td>\n",
" <td>0.213503</td>\n",
" <td>1686.155999</td>\n",
" <td>59.699133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Juno</td>\n",
" <td>2.669150</td>\n",
" <td>0.256942</td>\n",
" <td>12.988919</td>\n",
" <td>169.852760</td>\n",
" <td>248.138626</td>\n",
" <td>1.983332</td>\n",
" <td>3.354967</td>\n",
" <td>4.360814</td>\n",
" <td>72684.0</td>\n",
" <td>...</td>\n",
" <td>0.433</td>\n",
" <td>NaN</td>\n",
" <td>Sk</td>\n",
" <td>S</td>\n",
" <td>0.32</td>\n",
" <td>1.034540</td>\n",
" <td>MBA</td>\n",
" <td>0.226019</td>\n",
" <td>1592.787285</td>\n",
" <td>34.925016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Vesta</td>\n",
" <td>2.361418</td>\n",
" <td>0.088721</td>\n",
" <td>7.141771</td>\n",
" <td>103.810804</td>\n",
" <td>150.728541</td>\n",
" <td>2.151909</td>\n",
" <td>2.570926</td>\n",
" <td>3.628837</td>\n",
" <td>24288.0</td>\n",
" <td>...</td>\n",
" <td>0.492</td>\n",
" <td>NaN</td>\n",
" <td>V</td>\n",
" <td>V</td>\n",
" <td>0.32</td>\n",
" <td>1.139480</td>\n",
" <td>MBA</td>\n",
" <td>0.271609</td>\n",
" <td>1325.432765</td>\n",
" <td>95.861936</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Astraea</td>\n",
" <td>2.574249</td>\n",
" <td>0.191095</td>\n",
" <td>5.366988</td>\n",
" <td>141.576605</td>\n",
" <td>358.687607</td>\n",
" <td>2.082324</td>\n",
" <td>3.066174</td>\n",
" <td>4.130323</td>\n",
" <td>63507.0</td>\n",
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" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>S</td>\n",
" <td>NaN</td>\n",
" <td>1.095890</td>\n",
" <td>MBA</td>\n",
" <td>0.238632</td>\n",
" <td>1508.600458</td>\n",
" <td>282.366289</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>0.032397</td>\n",
" <td>APO</td>\n",
" <td>0.208911</td>\n",
" <td>1723.217927</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.956145</td>\n",
" <td>MBA</td>\n",
" <td>0.229090</td>\n",
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" <td>13.366251</td>\n",
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" <th>839711</th>\n",
" <td>NaN</td>\n",
" <td>2.373137</td>\n",
" <td>0.202053</td>\n",
" <td>0.732484</td>\n",
" <td>176.499082</td>\n",
" <td>198.026527</td>\n",
" <td>1.893638</td>\n",
" <td>2.852636</td>\n",
" <td>3.655884</td>\n",
" <td>5.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.893896</td>\n",
" <td>MBA</td>\n",
" <td>0.269600</td>\n",
" <td>1335.311579</td>\n",
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" <td>0.287672</td>\n",
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" <td>70.709555</td>\n",
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" <td>1.813901</td>\n",
" <td>3.278983</td>\n",
" <td>4.063580</td>\n",
" <td>11.0</td>\n",
" <td>...</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.815280</td>\n",
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" <td>0.242551</td>\n",
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" <td>20.432959</td>\n",
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"</div>"
]
},
"metadata": {},
"execution_count": 2
}
],
"metadata": {
"scrolled": true
}
},
{
"cell_type": "markdown",
"source": [
"## Checking out the dataset"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 3,
"source": [
"df.shape"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(839714, 31)"
]
},
"metadata": {},
"execution_count": 3
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 4,
"source": [
"df.info()"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 839714 entries, 0 to 839713\n",
"Data columns (total 31 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 name 21967 non-null object \n",
" 1 a 839712 non-null float64\n",
" 2 e 839714 non-null float64\n",
" 3 i 839714 non-null float64\n",
" 4 om 839714 non-null float64\n",
" 5 w 839714 non-null float64\n",
" 6 q 839714 non-null float64\n",
" 7 ad 839708 non-null float64\n",
" 8 per_y 839713 non-null float64\n",
" 9 data_arc 824240 non-null float64\n",
" 10 condition_code 838847 non-null object \n",
" 11 n_obs_used 839714 non-null int64 \n",
" 12 H 837025 non-null float64\n",
" 13 neo 839708 non-null object \n",
" 14 pha 823272 non-null object \n",
" 15 diameter 137636 non-null object \n",
" 16 extent 18 non-null object \n",
" 17 albedo 136409 non-null float64\n",
" 18 rot_per 18796 non-null float64\n",
" 19 GM 14 non-null float64\n",
" 20 BV 1021 non-null float64\n",
" 21 UB 979 non-null float64\n",
" 22 IR 1 non-null float64\n",
" 23 spec_B 1666 non-null object \n",
" 24 spec_T 980 non-null object \n",
" 25 G 119 non-null float64\n",
" 26 moid 823272 non-null float64\n",
" 27 class 839714 non-null object \n",
" 28 n 839712 non-null float64\n",
" 29 per 839708 non-null float64\n",
" 30 ma 839706 non-null float64\n",
"dtypes: float64(21), int64(1), object(9)\n",
"memory usage: 198.6+ MB\n"
]
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Checking for null values"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 5,
"source": [
"df.isnull().sum()"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"name 817747\n",
"a 2\n",
"e 0\n",
"i 0\n",
"om 0\n",
"w 0\n",
"q 0\n",
"ad 6\n",
"per_y 1\n",
"data_arc 15474\n",
"condition_code 867\n",
"n_obs_used 0\n",
"H 2689\n",
"neo 6\n",
"pha 16442\n",
"diameter 702078\n",
"extent 839696\n",
"albedo 703305\n",
"rot_per 820918\n",
"GM 839700\n",
"BV 838693\n",
"UB 838735\n",
"IR 839713\n",
"spec_B 838048\n",
"spec_T 838734\n",
"G 839595\n",
"moid 16442\n",
"class 0\n",
"n 2\n",
"per 6\n",
"ma 8\n",
"dtype: int64"
]
},
"metadata": {},
"execution_count": 5
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 6,
"source": [
"sns.heatmap(df.isnull())"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"metadata": {},
"execution_count": 6
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
],
"image/png": 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"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Dropping the irrelative columns"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 7,
"source": [
"df.drop(['name', 'extent', 'rot_per',\r\n",
" 'BV', 'IR', 'spec_T', \r\n",
" 'G', 'GM', 'UB', 'spec_B', \r\n",
" 'data_arc', 'albedo', 'diameter'],axis=1,inplace=True)\r\n",
"df.info()"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 839714 entries, 0 to 839713\n",
"Data columns (total 18 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 a 839712 non-null float64\n",
" 1 e 839714 non-null float64\n",
" 2 i 839714 non-null float64\n",
" 3 om 839714 non-null float64\n",
" 4 w 839714 non-null float64\n",
" 5 q 839714 non-null float64\n",
" 6 ad 839708 non-null float64\n",
" 7 per_y 839713 non-null float64\n",
" 8 condition_code 838847 non-null object \n",
" 9 n_obs_used 839714 non-null int64 \n",
" 10 H 837025 non-null float64\n",
" 11 neo 839708 non-null object \n",
" 12 pha 823272 non-null object \n",
" 13 moid 823272 non-null float64\n",
" 14 class 839714 non-null object \n",
" 15 n 839712 non-null float64\n",
" 16 per 839708 non-null float64\n",
" 17 ma 839706 non-null float64\n",
"dtypes: float64(13), int64(1), object(4)\n",
"memory usage: 115.3+ MB\n"
]
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Checking what is left"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 8,
"source": [
"df"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" a e i om w q \\\n",
"0 2.769165 0.076009 10.594067 80.305532 73.597694 2.558684 \n",
"1 2.772466 0.230337 34.836234 173.080063 310.048857 2.133865 \n",
"2 2.669150 0.256942 12.988919 169.852760 248.138626 1.983332 \n",
"3 2.361418 0.088721 7.141771 103.810804 150.728541 2.151909 \n",
"4 2.574249 0.191095 5.366988 141.576605 358.687607 2.082324 \n",
"... ... ... ... ... ... ... \n",
"839709 2.812945 0.664688 4.695700 183.310012 234.618352 0.943214 \n",
"839710 2.645238 0.259376 12.574937 1.620020 339.568072 1.959126 \n",
"839711 2.373137 0.202053 0.732484 176.499082 198.026527 1.893638 \n",
"839712 2.260404 0.258348 9.661947 204.512448 148.496988 1.676433 \n",
"839713 2.546442 0.287672 5.356238 70.709555 273.483265 1.813901 \n",
"\n",
" ad per_y condition_code n_obs_used H neo pha \\\n",
"0 2.979647 4.608202 0 1002 3.340 N N \n",
"1 3.411067 4.616444 0 8490 4.130 N N \n",
"2 3.354967 4.360814 0 7104 5.330 N N \n",
"3 2.570926 3.628837 0 9325 3.200 N N \n",
"4 3.066174 4.130323 0 2916 6.850 N N \n",
"... ... ... ... ... ... .. .. \n",
"839709 4.682676 4.717914 0 118 20.400 Y Y \n",
"839710 3.331350 4.302346 9 15 17.507 N N \n",
"839711 2.852636 3.655884 9 6 18.071 N N \n",
"839712 2.844376 3.398501 9 13 18.060 N N \n",
"839713 3.278983 4.063580 9 11 17.406 N N \n",
"\n",
" moid class n per ma \n",
"0 1.594780 MBA 0.213885 1683.145708 77.372096 \n",
"1 1.233240 MBA 0.213503 1686.155999 59.699133 \n",
"2 1.034540 MBA 0.226019 1592.787285 34.925016 \n",
"3 1.139480 MBA 0.271609 1325.432765 95.861936 \n",
"4 1.095890 MBA 0.238632 1508.600458 282.366289 \n",
"... ... ... ... ... ... \n",
"839709 0.032397 APO 0.208911 1723.217927 156.905910 \n",
"839710 0.956145 MBA 0.229090 1571.431965 13.366251 \n",
"839711 0.893896 MBA 0.269600 1335.311579 355.351127 \n",
"839712 0.680220 MBA 0.290018 1241.302609 15.320134 \n",
"839713 0.815280 MBA 0.242551 1484.222588 20.432959 \n",
"\n",
"[839714 rows x 18 columns]"
],
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>e</th>\n",
" <th>i</th>\n",
" <th>om</th>\n",
" <th>w</th>\n",
" <th>q</th>\n",
" <th>ad</th>\n",
" <th>per_y</th>\n",
" <th>condition_code</th>\n",
" <th>n_obs_used</th>\n",
" <th>H</th>\n",
" <th>neo</th>\n",
" <th>pha</th>\n",
" <th>moid</th>\n",
" <th>class</th>\n",
" <th>n</th>\n",
" <th>per</th>\n",
" <th>ma</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.769165</td>\n",
" <td>0.076009</td>\n",
" <td>10.594067</td>\n",
" <td>80.305532</td>\n",
" <td>73.597694</td>\n",
" <td>2.558684</td>\n",
" <td>2.979647</td>\n",
" <td>4.608202</td>\n",
" <td>0</td>\n",
" <td>1002</td>\n",
" <td>3.340</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>1.594780</td>\n",
" <td>MBA</td>\n",
" <td>0.213885</td>\n",
" <td>1683.145708</td>\n",
" <td>77.372096</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.772466</td>\n",
" <td>0.230337</td>\n",
" <td>34.836234</td>\n",
" <td>173.080063</td>\n",
" <td>310.048857</td>\n",
" <td>2.133865</td>\n",
" <td>3.411067</td>\n",
" <td>4.616444</td>\n",
" <td>0</td>\n",
" <td>8490</td>\n",
" <td>4.130</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>1.233240</td>\n",
" <td>MBA</td>\n",
" <td>0.213503</td>\n",
" <td>1686.155999</td>\n",
" <td>59.699133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.669150</td>\n",
" <td>0.256942</td>\n",
" <td>12.988919</td>\n",
" <td>169.852760</td>\n",
" <td>248.138626</td>\n",
" <td>1.983332</td>\n",
" <td>3.354967</td>\n",
" <td>4.360814</td>\n",
" <td>0</td>\n",
" <td>7104</td>\n",
" <td>5.330</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>1.034540</td>\n",
" <td>MBA</td>\n",
" <td>0.226019</td>\n",
" <td>1592.787285</td>\n",
" <td>34.925016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.361418</td>\n",
" <td>0.088721</td>\n",
" <td>7.141771</td>\n",
" <td>103.810804</td>\n",
" <td>150.728541</td>\n",
" <td>2.151909</td>\n",
" <td>2.570926</td>\n",
" <td>3.628837</td>\n",
" <td>0</td>\n",
" <td>9325</td>\n",
" <td>3.200</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>1.139480</td>\n",
" <td>MBA</td>\n",
" <td>0.271609</td>\n",
" <td>1325.432765</td>\n",
" <td>95.861936</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.574249</td>\n",
" <td>0.191095</td>\n",
" <td>5.366988</td>\n",
" <td>141.576605</td>\n",
" <td>358.687607</td>\n",
" <td>2.082324</td>\n",
" <td>3.066174</td>\n",
" <td>4.130323</td>\n",
" <td>0</td>\n",
" <td>2916</td>\n",
" <td>6.850</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>1.095890</td>\n",
" <td>MBA</td>\n",
" <td>0.238632</td>\n",
" <td>1508.600458</td>\n",
" <td>282.366289</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>839709</th>\n",
" <td>2.812945</td>\n",
" <td>0.664688</td>\n",
" <td>4.695700</td>\n",
" <td>183.310012</td>\n",
" <td>234.618352</td>\n",
" <td>0.943214</td>\n",
" <td>4.682676</td>\n",
" <td>4.717914</td>\n",
" <td>0</td>\n",
" <td>118</td>\n",
" <td>20.400</td>\n",
" <td>Y</td>\n",
" <td>Y</td>\n",
" <td>0.032397</td>\n",
" <td>APO</td>\n",
" <td>0.208911</td>\n",
" <td>1723.217927</td>\n",
" <td>156.905910</td>\n",
" </tr>\n",
" <tr>\n",
" <th>839710</th>\n",
" <td>2.645238</td>\n",
" <td>0.259376</td>\n",
" <td>12.574937</td>\n",
" <td>1.620020</td>\n",
" <td>339.568072</td>\n",
" <td>1.959126</td>\n",
" <td>3.331350</td>\n",
" <td>4.302346</td>\n",
" <td>9</td>\n",
" <td>15</td>\n",
" <td>17.507</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>0.956145</td>\n",
" <td>MBA</td>\n",
" <td>0.229090</td>\n",
" <td>1571.431965</td>\n",
" <td>13.366251</td>\n",
" </tr>\n",
" <tr>\n",
" <th>839711</th>\n",
" <td>2.373137</td>\n",
" <td>0.202053</td>\n",
" <td>0.732484</td>\n",
" <td>176.499082</td>\n",
" <td>198.026527</td>\n",
" <td>1.893638</td>\n",
" <td>2.852636</td>\n",
" <td>3.655884</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>18.071</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>0.893896</td>\n",
" <td>MBA</td>\n",
" <td>0.269600</td>\n",
" <td>1335.311579</td>\n",
" <td>355.351127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>839712</th>\n",
" <td>2.260404</td>\n",
" <td>0.258348</td>\n",
" <td>9.661947</td>\n",
" <td>204.512448</td>\n",
" <td>148.496988</td>\n",
" <td>1.676433</td>\n",
" <td>2.844376</td>\n",
" <td>3.398501</td>\n",
" <td>9</td>\n",
" <td>13</td>\n",
" <td>18.060</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>0.680220</td>\n",
" <td>MBA</td>\n",
" <td>0.290018</td>\n",
" <td>1241.302609</td>\n",
" <td>15.320134</td>\n",
" </tr>\n",
" <tr>\n",
" <th>839713</th>\n",
" <td>2.546442</td>\n",
" <td>0.287672</td>\n",
" <td>5.356238</td>\n",
" <td>70.709555</td>\n",
" <td>273.483265</td>\n",
" <td>1.813901</td>\n",
" <td>3.278983</td>\n",
" <td>4.063580</td>\n",
" <td>9</td>\n",
" <td>11</td>\n",
" <td>17.406</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>0.815280</td>\n",
" <td>MBA</td>\n",
" <td>0.242551</td>\n",
" <td>1484.222588</td>\n",
" <td>20.432959</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>839714 rows × 18 columns</p>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 8
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Checking what does \"pha\" contain"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 9,
"source": [
"df[\"pha\"].unique()"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['N', 'Y', nan], dtype=object)"
]
},
"metadata": {},
"execution_count": 9
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Dropping the Not A Number cells"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 10,
"source": [
"df = df[df.pha!= 'nan']\r\n",
"df.dropna(inplace=True)\r\n",
"df.info()"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 820565 entries, 0 to 839713\n",
"Data columns (total 18 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 a 820565 non-null float64\n",
" 1 e 820565 non-null float64\n",
" 2 i 820565 non-null float64\n",
" 3 om 820565 non-null float64\n",
" 4 w 820565 non-null float64\n",
" 5 q 820565 non-null float64\n",
" 6 ad 820565 non-null float64\n",
" 7 per_y 820565 non-null float64\n",
" 8 condition_code 820565 non-null object \n",
" 9 n_obs_used 820565 non-null int64 \n",
" 10 H 820565 non-null float64\n",
" 11 neo 820565 non-null object \n",
" 12 pha 820565 non-null object \n",
" 13 moid 820565 non-null float64\n",
" 14 class 820565 non-null object \n",
" 15 n 820565 non-null float64\n",
" 16 per 820565 non-null float64\n",
" 17 ma 820565 non-null float64\n",
"dtypes: float64(13), int64(1), object(4)\n",
"memory usage: 118.9+ MB\n"
]
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Creating a function that reindexes the passed data frame "
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 11,
"source": [
"'''\r\n",
"TAKES:\r\n",
"@ df --> the dataframe that needs reindexing\r\n",
"'''\r\n",
"def reindex (df):\r\n",
" df.reset_index(inplace = True)\r\n",
" df.drop(['index'],axis=1,inplace=True)\r\n",
"\r\n",
"\r\n",
"# Reindexing the data frame\r\n",
"reindex(df)"
],
"outputs": [],
"metadata": {
"scrolled": true
}
},
{
"cell_type": "markdown",
"source": [
"## Checking how inconsistent is \"condition_code\""
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 12,
"source": [
"df[\"condition_code\"].unique()"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0, 2, 1, 4, 3, 5, '0', '1', '2', '3', '4', '8', '9', '7', '6', '5',\n",
" 9.0, 7.0, 6.0, 8.0], dtype=object)"
]
},
"metadata": {},
"execution_count": 12
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Fixing the inconsistencies of \"condition code\""
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 13,
"source": [
"\r\n",
"condition_code = []\r\n",
"for i in df.condition_code:\r\n",
" condition_code.append(int(i))\r\n",
"condition_code_df = pd.DataFrame(data = condition_code, columns = ['Condition_Code'])\r\n",
"df.drop(['condition_code'],axis=1,inplace=True)\r\n",
"df = pd.concat([df,condition_code_df],axis=1)\r\n",
"df"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" a e i om w q \\\n",
"0 2.769165 0.076009 10.594067 80.305532 73.597694 2.558684 \n",
"1 2.772466 0.230337 34.836234 173.080063 310.048857 2.133865 \n",
"2 2.669150 0.256942 12.988919 169.852760 248.138626 1.983332 \n",
"3 2.361418 0.088721 7.141771 103.810804 150.728541 2.151909 \n",
"4 2.574249 0.191095 5.366988 141.576605 358.687607 2.082324 \n",
"... ... ... ... ... ... ... \n",
"820560 2.812945 0.664688 4.695700 183.310012 234.618352 0.943214 \n",
"820561 2.645238 0.259376 12.574937 1.620020 339.568072 1.959126 \n",
"820562 2.373137 0.202053 0.732484 176.499082 198.026527 1.893638 \n",
"820563 2.260404 0.258348 9.661947 204.512448 148.496988 1.676433 \n",
"820564 2.546442 0.287672 5.356238 70.709555 273.483265 1.813901 \n",
"\n",
" ad per_y n_obs_used H neo pha moid class \\\n",
"0 2.979647 4.608202 1002 3.340 N N 1.594780 MBA \n",
"1 3.411067 4.616444 8490 4.130 N N 1.233240 MBA \n",
"2 3.354967 4.360814 7104 5.330 N N 1.034540 MBA \n",
"3 2.570926 3.628837 9325 3.200 N N 1.139480 MBA \n",
"4 3.066174 4.130323 2916 6.850 N N 1.095890 MBA \n",
"... ... ... ... ... .. .. ... ... \n",
"820560 4.682676 4.717914 118 20.400 Y Y 0.032397 APO \n",
"820561 3.331350 4.302346 15 17.507 N N 0.956145 MBA \n",
"820562 2.852636 3.655884 6 18.071 N N 0.893896 MBA \n",
"820563 2.844376 3.398501 13 18.060 N N 0.680220 MBA \n",
"820564 3.278983 4.063580 11 17.406 N N 0.815280 MBA \n",
"\n",
" n per ma Condition_Code \n",
"0 0.213885 1683.145708 77.372096 0 \n",
"1 0.213503 1686.155999 59.699133 0 \n",
"2 0.226019 1592.787285 34.925016 0 \n",
"3 0.271609 1325.432765 95.861936 0 \n",
"4 0.238632 1508.600458 282.366289 0 \n",
"... ... ... ... ... \n",
"820560 0.208911 1723.217927 156.905910 0 \n",
"820561 0.229090 1571.431965 13.366251 9 \n",
"820562 0.269600 1335.311579 355.351127 9 \n",
"820563 0.290018 1241.302609 15.320134 9 \n",
"820564 0.242551 1484.222588 20.432959 9 \n",
"\n",
"[820565 rows x 18 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>a</th>\n",
" <th>e</th>\n",
" <th>i</th>\n",
" <th>om</th>\n",
" <th>w</th>\n",
" <th>q</th>\n",
" <th>ad</th>\n",
" <th>per_y</th>\n",
" <th>n_obs_used</th>\n",
" <th>H</th>\n",
" <th>neo</th>\n",
" <th>pha</th>\n",
" <th>moid</th>\n",
" <th>class</th>\n",
" <th>n</th>\n",
" <th>per</th>\n",
" <th>ma</th>\n",
" <th>Condition_Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.769165</td>\n",
" <td>0.076009</td>\n",
" <td>10.594067</td>\n",
" <td>80.305532</td>\n",
" <td>73.597694</td>\n",
" <td>2.558684</td>\n",
" <td>2.979647</td>\n",
" <td>4.608202</td>\n",
" <td>1002</td>\n",
" <td>3.340</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>1.594780</td>\n",
" <td>MBA</td>\n",
" <td>0.213885</td>\n",
" <td>1683.145708</td>\n",
" <td>77.372096</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.772466</td>\n",
" <td>0.230337</td>\n",
" <td>34.836234</td>\n",
" <td>173.080063</td>\n",
" <td>310.048857</td>\n",
" <td>2.133865</td>\n",
" <td>3.411067</td>\n",
" <td>4.616444</td>\n",
" <td>8490</td>\n",
" <td>4.130</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>1.233240</td>\n",
" <td>MBA</td>\n",
" <td>0.213503</td>\n",
" <td>1686.155999</td>\n",
" <td>59.699133</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.669150</td>\n",
" <td>0.256942</td>\n",
" <td>12.988919</td>\n",
" <td>169.852760</td>\n",
" <td>248.138626</td>\n",
" <td>1.983332</td>\n",
" <td>3.354967</td>\n",
" <td>4.360814</td>\n",
" <td>7104</td>\n",
" <td>5.330</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>1.034540</td>\n",
" <td>MBA</td>\n",
" <td>0.226019</td>\n",
" <td>1592.787285</td>\n",
" <td>34.925016</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.361418</td>\n",
" <td>0.088721</td>\n",
" <td>7.141771</td>\n",
" <td>103.810804</td>\n",
" <td>150.728541</td>\n",
" <td>2.151909</td>\n",
" <td>2.570926</td>\n",
" <td>3.628837</td>\n",
" <td>9325</td>\n",
" <td>3.200</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>1.139480</td>\n",
" <td>MBA</td>\n",
" <td>0.271609</td>\n",
" <td>1325.432765</td>\n",
" <td>95.861936</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.574249</td>\n",
" <td>0.191095</td>\n",
" <td>5.366988</td>\n",
" <td>141.576605</td>\n",
" <td>358.687607</td>\n",
" <td>2.082324</td>\n",
" <td>3.066174</td>\n",
" <td>4.130323</td>\n",
" <td>2916</td>\n",
" <td>6.850</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>1.095890</td>\n",
" <td>MBA</td>\n",
" <td>0.238632</td>\n",
" <td>1508.600458</td>\n",
" <td>282.366289</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>820560</th>\n",
" <td>2.812945</td>\n",
" <td>0.664688</td>\n",
" <td>4.695700</td>\n",
" <td>183.310012</td>\n",
" <td>234.618352</td>\n",
" <td>0.943214</td>\n",
" <td>4.682676</td>\n",
" <td>4.717914</td>\n",
" <td>118</td>\n",
" <td>20.400</td>\n",
" <td>Y</td>\n",
" <td>Y</td>\n",
" <td>0.032397</td>\n",
" <td>APO</td>\n",
" <td>0.208911</td>\n",
" <td>1723.217927</td>\n",
" <td>156.905910</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>820561</th>\n",
" <td>2.645238</td>\n",
" <td>0.259376</td>\n",
" <td>12.574937</td>\n",
" <td>1.620020</td>\n",
" <td>339.568072</td>\n",
" <td>1.959126</td>\n",
" <td>3.331350</td>\n",
" <td>4.302346</td>\n",
" <td>15</td>\n",
" <td>17.507</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>0.956145</td>\n",
" <td>MBA</td>\n",
" <td>0.229090</td>\n",
" <td>1571.431965</td>\n",
" <td>13.366251</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>820562</th>\n",
" <td>2.373137</td>\n",
" <td>0.202053</td>\n",
" <td>0.732484</td>\n",
" <td>176.499082</td>\n",
" <td>198.026527</td>\n",
" <td>1.893638</td>\n",
" <td>2.852636</td>\n",
" <td>3.655884</td>\n",
" <td>6</td>\n",
" <td>18.071</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>0.893896</td>\n",
" <td>MBA</td>\n",
" <td>0.269600</td>\n",
" <td>1335.311579</td>\n",
" <td>355.351127</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>820563</th>\n",
" <td>2.260404</td>\n",
" <td>0.258348</td>\n",
" <td>9.661947</td>\n",
" <td>204.512448</td>\n",
" <td>148.496988</td>\n",
" <td>1.676433</td>\n",
" <td>2.844376</td>\n",
" <td>3.398501</td>\n",
" <td>13</td>\n",
" <td>18.060</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>0.680220</td>\n",
" <td>MBA</td>\n",
" <td>0.290018</td>\n",
" <td>1241.302609</td>\n",
" <td>15.320134</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>820564</th>\n",
" <td>2.546442</td>\n",
" <td>0.287672</td>\n",
" <td>5.356238</td>\n",
" <td>70.709555</td>\n",
" <td>273.483265</td>\n",
" <td>1.813901</td>\n",
" <td>3.278983</td>\n",
" <td>4.063580</td>\n",
" <td>11</td>\n",
" <td>17.406</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" <td>0.815280</td>\n",
" <td>MBA</td>\n",
" <td>0.242551</td>\n",
" <td>1484.222588</td>\n",
" <td>20.432959</td>\n",
" <td>9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>820565 rows × 18 columns</p>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 13
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Checking what does \"neo\" contain"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 14,
"source": [
"df[\"neo\"].unique()"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['N', 'Y'], dtype=object)"
]
},
"metadata": {},
"execution_count": 14
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Checking what does \"class\" contain"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 15,
"source": [
"df[\"class\"].unique()"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['MBA', 'OMB', 'MCA', 'AMO', 'IMB', 'TJN', 'CEN', 'APO', 'ATE',\n",
" 'AST', 'TNO', 'IEO'], dtype=object)"
]
},
"metadata": {},
"execution_count": 15
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Data Visualisation"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Plotting \"pha\""
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 16,
"source": [
"#'pha' contains boolean values \r\n",
"# that claim if an ateroid is dangerous\r\n",
"sns.countplot('pha', data=df, palette='Set2')\r\n",
"plt.legend(labels=[\"No\",\"Yes\"], title = \"Is The Asteroid Dangerous?\")"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x19280931be0>"
]
},
"metadata": {},
"execution_count": 16
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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mdgLwA+AXceVJwGNmNhTYDAwI9XnEpiGXEZuKPATA3XeY2c3A8tDuJnffEZZH8PVU5OfDK1kfIiKSAWkNF3f/GDi5Wq2S2Oyx6m0dGFnDcWYAMxLUS4G8BPWEfYiISGboE/oiIhI5hYuIiERO4SIiIpFTuIiISOQULiIiEjmFi4iIRE7hIiIikVO4iIhI5BQuIiISOYWLiIhETuEiIiKRU7iIiEjkFC4iIhI5hYuIiERO4SIiIpFTuIiISOTSGi5m1tzMnjCz9Wa2zszON7OWZrbAzDaE9xahrZnZFDMrM7NVZnZu3HEGh/YbzGxwXL2bma0O+0wJP3dMTX2IiEhmpPvK5Q/AX939LKALsA4YCyx09w7AwrAO0A/oEF7DgakQCwpgPNAD6A6MjwuLqcCwuP2KQ72mPkREJAPSFi5m1gzoBUwHcPfP3H0XUALMDM1mAheH5RJglscsBZqb2WlAX2CBu+9w953AAqA4bDvJ3ZeGn0ieVe1YifoQEZEMSOeVS3ugAnjAzF43s/8ysxOAVu6+LbR5D2gVllsDW+L2Lw+1ZPXyBHWS9HEAMxtuZqVmVlpRUfFNzlFERBJIZ7jUB84Fprp7V+Bjqt2eClccnsYxJO3D3ae5e6G7F+bk5KRzGCIidUo6w6UcKHf3V8P6E8TC5v1wS4vwvj1s3wq0ids/N9SS1XMT1EnSh4iIZEDawsXd3wO2mFnHUOoNrAXmAvtnfA0GngnLc4FBYdZYEbA73NqaD/QxsxbhQX4fYH7Y9qGZFYVZYoOqHStRHyIikgH103z8a4CHzawhsBEYQizQHjOzocBmYEBoOw+4CCgDPgltcfcdZnYzsDy0u8ndd4TlEcCDQGPg+fACmFRDHyIikgFpDRd3XwkUJtjUO0FbB0bWcJwZwIwE9VIgL0G9MlEfIiKSGfqEvoiIRE7hIiIikVO4iIhI5BQuIiISOYWLiIhETuEiIiKRU7iIiEjkFC4iIhI5hYuIiERO4SIiIpFTuIiISOQULiIiEjmFi4iIRE7hIiIikVO4iIhI5BQuIiISubSGi5ltMrPVZrbSzEpDraWZLTCzDeG9RaibmU0xszIzW2Vm58YdZ3Bov8HMBsfVu4Xjl4V9LVkfIiKSGZm4cvmf7l7g7vt/kXIssNDdOwALwzpAP6BDeA0HpkIsKIDxQA+gOzA+LiymAsPi9is+RB8iIpIB2bgtVgLMDMszgYvj6rM8ZinQ3MxOA/oCC9x9h7vvBBYAxWHbSe6+NPxE8qxqx0rUh4iIZEC6w8WBF8xshZkND7VW7r4tLL8HtArLrYEtcfuWh1qyenmCerI+DmBmw82s1MxKKyoqDvvkREQksfppPv7/cPetZvYtYIGZrY/f6O5uZp7OASTrw92nAdMACgsL0zoOEZG6JK1XLu6+NbxvB54m9szk/XBLi/C+PTTfCrSJ2z031JLVcxPUSdKHiIhkQNrCxcxOMLMT9y8DfYA3gbnA/hlfg4FnwvJcYFCYNVYE7A63tuYDfcysRXiQ3weYH7Z9aGZFYZbYoGrHStSHiIhkQDpvi7UCng6zg+sDj7j7X81sOfCYmQ0FNgMDQvt5wEVAGfAJMATA3XeY2c3A8tDuJnffEZZHAA8CjYHnwwtgUg19iIhIBqQtXNx9I9AlQb0S6J2g7sDIGo41A5iRoF4K5KXah4iIZIY+oS8iIpFLKVzMbGEqNRERETjEbTEzawQ0AU4JD9MtbDqJrz9TIiIicoBDPXP5BXAdcDqwgq/D5UPg3vQNS0REjmVJw8Xd/wD8wcyucfd7MjQmERE5xqU0W8zd7zGz7wLt4vdx91lpGpeIiBzDUgoXM3sI+A6wEvgylPd/WaSIiMgBUv2cSyHQOXwWRUREJKlUP+fyJnBqOgciIiK1R6pXLqcAa81sGbBvf9Hdf5SWUYmIyDEt1XCZkM5BiIhI7ZLqbLG/pXsgIiJSe6Q6W+wjYrPDABoCDYCP3f2kdA1MRESOXaleuZy4fzn8dkoJUJSuQYmIyLHtsL8V2WP+D9A3+uGIiEhtkOptsUviVo8j9rmXvWkZkYiIHPNSvXL517hXX+AjYrfGDsnM6pnZ62b2bFhvb2avmlmZmT1qZg1D/fiwXha2t4s7xrhQf8vM+sbVi0OtzMzGxtUT9iEiIpmRUri4+5C41zB3n+ju21PsYzSwLm79VuAudz8T2AkMDfWhwM5Qvyu0w8w6AwOBs4Fi4L4QWPWAPwL9gM7AZaFtsj5ERCQDUv2xsFwze9rMtofXk2aWm8p+wP8C/iusG3Ah8ERoMhO4OCyXhHXC9t5xkwfmuPs+d/8nUAZ0D68yd9/o7p8Bc4CSQ/QhIiIZkOptsQeAucR+1+V04L9D7VDuBv4D+Cqsnwzscvcvwno5X//oWGtgC0DYvju0r6pX26emerI+REQkA1INlxx3f8DdvwivB4GcZDuY2Q+B7e6+4kgHmS5mNtzMSs2stKKiItvDERGpNVINl0ozu3L/sw4zuxKoPMQ+3wN+ZGabiN2yuhD4A9DczPbPUssFtoblrUAbgLC9Weijql5tn5rqlUn6OIC7T3P3QncvzMlJmpUiInIYUg2XnwEDgPeAbcBPgKuS7eDu49w9193bEXsg/5K7XwG8HPYHGAw8E5bnhnXC9pfCV/zPBQaG2WTtgQ7AMmA50CHMDGsY+pgb9qmpDxERyYBUw+UmYLC757j7t4iFzY3fsM9fA9ebWRmx5yPTQ306cHKoXw+MBXD3NcBjwFrgr8BId/8yPFMZBcwnNhvtsdA2WR8iIpIBqX4rcr6779y/4u47zKxrqp24+yJgUVjeSGymV/U2e4FLa9h/IjAxQX0eMC9BPWEfIiKSGaleuRxnZi32r5hZS1IPJhERqWNSDYg7gCVm9nhYv5QEVxIiIiKQ+rcizzKzUmIzvgAucfe16RuWiIgcy1K+tRXCRIEiIiKHdNhfuS8iInIoChcREYmcwkVERCKncBERkcgpXEREJHIKFxERiZzCRUREIqdwERGRyClcREQkcgoXERGJnMJFREQip3AREZHIKVxERCRyaQsXM2tkZsvM7A0zW2NmN4Z6ezN71czKzOxRM2sY6seH9bKwvV3cscaF+ltm1jeuXhxqZWY2Nq6esA8REcmMdF657AMudPcuQAFQbGZFwK3AXe5+JrATGBraDwV2hvpdoR1m1hkYCJwNFAP3mVk9M6sH/BHoB3QGLgttSdKHiIhkQNrCxWP2hNUG4eXEfnDsiVCfCVwclkvCOmF7bzOzUJ/j7vvc/Z9AGdA9vMrcfaO7fwbMAUrCPjX1ISIiGZDWZy7hCmMlsB1YALwN7HL3L0KTcqB1WG4NbAEI23cDJ8fXq+1TU/3kJH1UH99wMys1s9KKioojOFMREYmX1nBx9y/dvQDIJXalcVY6+ztc7j7N3QvdvTAnJyfbwxERqTUyMlvM3XcBLwPnA83NbP/PK+cCW8PyVqANQNjeDKiMr1fbp6Z6ZZI+REQkA9I5WyzHzJqH5cbAD4B1xELmJ6HZYOCZsDw3rBO2v+TuHuoDw2yy9kAHYBmwHOgQZoY1JPbQf27Yp6Y+REQkA+ofusk3dhowM8zqOg54zN2fNbO1wBwzuwV4HZge2k8HHjKzMmAHsbDA3deY2WPAWuALYKS7fwlgZqOA+UA9YIa7rwnH+nUNfYiISAakLVzcfRXQNUF9I7HnL9Xre4FLazjWRGBigvo8YF6qfYiISGboE/oiIhI5hYuIiERO4SIiIpFTuIiISOQULiIiEjmFi4iIRE7hIiIikVO4iIhI5BQuIiISOYWLiIhETuEiIiKRU7iIiEjkFC4iIhI5hYuIiERO4SIiIpFTuIiISOTS+TPHbczsZTNba2ZrzGx0qLc0swVmtiG8twh1M7MpZlZmZqvM7Ny4Yw0O7TeY2eC4ejczWx32mWJmlqwPERHJjHReuXwB/NLdOwNFwEgz6wyMBRa6ewdgYVgH6Ad0CK/hwFSIBQUwHuhB7Nclx8eFxVRgWNx+xaFeUx8iIpIBaQsXd9/m7q+F5Y+AdUBroASYGZrNBC4OyyXALI9ZCjQ3s9OAvsACd9/h7juBBUBx2HaSuy91dwdmVTtWoj5ERCQDMvLMxczaAV2BV4FW7r4tbHoPaBWWWwNb4nYrD7Vk9fIEdZL0UX1cw82s1MxKKyoqvsGZiYhIImkPFzNrCjwJXOfuH8ZvC1ccns7+k/Xh7tPcvdDdC3NyctI5DBGROiWt4WJmDYgFy8Pu/lQovx9uaRHet4f6VqBN3O65oZasnpugnqwPERHJgHTOFjNgOrDO3e+M2zQX2D/jazDwTFx9UJg1VgTsDre25gN9zKxFeJDfB5gftn1oZkWhr0HVjpWoDxERyYD6aTz294CfAqvNbGWo/QaYBDxmZkOBzcCAsG0ecBFQBnwCDAFw9x1mdjOwPLS7yd13hOURwINAY+D58CJJHyIikgFpCxd3fwWwGjb3TtDegZE1HGsGMCNBvRTIS1CvTNSHiIhkhj6hLyIikVO4iIhI5BQuIiISOYWLiIhETuEiIiKRU7iIiEjkFC4iIhI5hYuIiERO4SIiIpFTuIiISOQULiIiEjmFi4iIRE7hIiIikVO4iIhI5BQuIiISOYWLiIhELp0/czzDzLab2ZtxtZZmtsDMNoT3FqFuZjbFzMrMbJWZnRu3z+DQfoOZDY6rdzOz1WGfKeGnjmvsQ0REMiedVy4PAsXVamOBhe7eAVgY1gH6AR3CazgwFWJBAYwHegDdgfFxYTEVGBa3X/Eh+hARkQxJW7i4+2JgR7VyCTAzLM8ELo6rz/KYpUBzMzsN6AsscPcd7r4TWAAUh20nufvS8PPIs6odK1EfIiKSIZl+5tLK3beF5feAVmG5NbAlrl15qCWrlyeoJ+tDREQyJGsP9MMVh2ezDzMbbmalZlZaUVGRzqGIiNQpmQ6X98MtLcL79lDfCrSJa5cbasnquQnqyfo4iLtPc/dCdy/Mycn5xiclIiIHynS4zAX2z/gaDDwTVx8UZo0VAbvDra35QB8zaxEe5PcB5odtH5pZUZglNqjasRL1ISIiGVI/XQc2s9nABcApZlZObNbXJOAxMxsKbAYGhObzgIuAMuATYAiAu+8ws5uB5aHdTe6+f5LACGIz0hoDz4cXSfoQEZEMSVu4uPtlNWzqnaCtAyNrOM4MYEaCeimQl6BemagPERHJHH1CX0REIqdwERGRyClcREQkcgoXERGJnMJFREQip3AREZHIKVxERCRyChcREYmcwkVERCKncBERkcgpXEREJHIKFxERiZzCRUREIqdwERGRyClcREQkcgoXERGJnMJFREQiV2vDxcyKzewtMyszs7HZHo+ISF1SK8PFzOoBfwT6AZ2By8ysc3ZHJSJSd9TP9gDSpDtQ5u4bAcxsDlACrM3qqESyZPvU/8j2EOQo9K2rb0vbsWtruLQGtsStlwM9qjcys+HA8LC6x8zeysDY6opTgA+yPYhsu5PB2R6CHEx/NvcbMTmKo7RNVKyt4ZISd58GTMv2OGojMyt198Jsj0OkOv3ZzIxa+cwF2Aq0iVvPDTUREcmA2houy4EOZtbezBoCA4G5WR6TiEidUStvi7n7F2Y2CpgP1ANmuPuaLA+rrtHtRjla6c9mBpi7Z3sMIiJSy9TW22IiIpJFChcREYmcwkUiY2ZuZnfErf/KzCZkcUgiAFjMK2bWL652qZn9NZvjqs0ULhKlfcAlZnZKtgciEs9jD5f/DbjTzBqZWVPg98DI7I6s9lK4SJS+IDYT59+zPRCR6tz9TeC/gV8D/wnMcve3szuq2qtWTkWWrPojsMrM0velRSLf3I3Aa8BngD6ln0YKF4mUu39oZrOAa4FPsz0ekXju/rGZPQrscfd92R5PbabbYpIOdwNDgROyPA6RRL4KL0kjhYtEzt13AI8RCxgRqYMULpIudxD7anMRqYP09S8iIhI5XbmIiEjkFC4iIhI5hYuIiERO4SIiIpFTuIiISOQULiJHKTPbpC8BlWOVwkVERCKncBHJMjNrZ2brzexhM1tnZk+YWZOw+Roze83MVpvZWaF9dzNbYmavm9k/zKxjFocvkpDCReTo0BG4z907AR8CI0L9A3c/F5gK/CrU1gM93b0rsa+O/32mBytyKPpWZJGjwxZ3/79h+S/EvlUa4KnwvgK4JCw3A2aaWQfAgQYZG6VIinTlInJ0qP49TPvX938t/Jd8/Y/Bm4GX3T0P+FegUfqHJ3J4FC4iR4dvm9n5Yfly4JUkbZsBW8PyVekclMg3pXAROTq8BYw0s3VAC2LPWGpyG/C/zex1dGtbjlL6VmSRLDOzdsCz4TaXSK2gKxcREYmcrlxERCRyunIREZHIKVxERCRyChcREYmcwkVERCKncBERkcj9f4q7pog05wpyAAAAAElFTkSuQmCC"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Plotting \"neo\""
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 17,
"source": [
"sns.countplot('neo', data=df, palette='Set2')"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:xlabel='neo', ylabel='count'>"
]
},
"metadata": {},
"execution_count": 17
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## One Hot Encoding \"neo\" & \"class\""
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 18,
"source": [
"from sklearn.preprocessing import OneHotEncoder\r\n",
"for i in ['neo','class']:\r\n",
" oh = OneHotEncoder()\r\n",
" oh_df = pd.DataFrame(oh.fit_transform(df[[i]]).toarray())\r\n",
" df.drop([i],axis=1,inplace=True)\r\n",
" df = pd.concat([df,oh_df],axis=1)\r\n",
"df"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" a e i om w q \\\n",
"0 2.769165 0.076009 10.594067 80.305532 73.597694 2.558684 \n",
"1 2.772466 0.230337 34.836234 173.080063 310.048857 2.133865 \n",
"2 2.669150 0.256942 12.988919 169.852760 248.138626 1.983332 \n",
"3 2.361418 0.088721 7.141771 103.810804 150.728541 2.151909 \n",
"4 2.574249 0.191095 5.366988 141.576605 358.687607 2.082324 \n",
"... ... ... ... ... ... ... \n",
"820560 2.812945 0.664688 4.695700 183.310012 234.618352 0.943214 \n",
"820561 2.645238 0.259376 12.574937 1.620020 339.568072 1.959126 \n",
"820562 2.373137 0.202053 0.732484 176.499082 198.026527 1.893638 \n",
"820563 2.260404 0.258348 9.661947 204.512448 148.496988 1.676433 \n",
"820564 2.546442 0.287672 5.356238 70.709555 273.483265 1.813901 \n",
"\n",
" ad per_y n_obs_used H ... 2 3 4 5 6 \\\n",
"0 2.979647 4.608202 1002 3.340 ... 0.0 0.0 0.0 0.0 0.0 \n",
"1 3.411067 4.616444 8490 4.130 ... 0.0 0.0 0.0 0.0 0.0 \n",
"2 3.354967 4.360814 7104 5.330 ... 0.0 0.0 0.0 0.0 0.0 \n",
"3 2.570926 3.628837 9325 3.200 ... 0.0 0.0 0.0 0.0 0.0 \n",
"4 3.066174 4.130323 2916 6.850 ... 0.0 0.0 0.0 0.0 0.0 \n",
"... ... ... ... ... ... ... ... ... ... ... \n",
"820560 4.682676 4.717914 118 20.400 ... 0.0 0.0 0.0 0.0 0.0 \n",
"820561 3.331350 4.302346 15 17.507 ... 0.0 0.0 0.0 0.0 0.0 \n",
"820562 2.852636 3.655884 6 18.071 ... 0.0 0.0 0.0 0.0 0.0 \n",
"820563 2.844376 3.398501 13 18.060 ... 0.0 0.0 0.0 0.0 0.0 \n",
"820564 3.278983 4.063580 11 17.406 ... 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" 7 8 9 10 11 \n",
"0 1.0 0.0 0.0 0.0 0.0 \n",
"1 1.0 0.0 0.0 0.0 0.0 \n",
"2 1.0 0.0 0.0 0.0 0.0 \n",
"3 1.0 0.0 0.0 0.0 0.0 \n",
"4 1.0 0.0 0.0 0.0 0.0 \n",
"... ... ... ... ... ... \n",
"820560 0.0 0.0 0.0 0.0 0.0 \n",
"820561 1.0 0.0 0.0 0.0 0.0 \n",
"820562 1.0 0.0 0.0 0.0 0.0 \n",
"820563 1.0 0.0 0.0 0.0 0.0 \n",
"820564 1.0 0.0 0.0 0.0 0.0 \n",
"\n",
"[820565 rows x 30 columns]"
],
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>e</th>\n",
" <th>i</th>\n",
" <th>om</th>\n",
" <th>w</th>\n",
" <th>q</th>\n",
" <th>ad</th>\n",
" <th>per_y</th>\n",
" <th>n_obs_used</th>\n",
" <th>H</th>\n",
" <th>...</th>\n",
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" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" <th>10</th>\n",
" <th>11</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.769165</td>\n",
" <td>0.076009</td>\n",
" <td>10.594067</td>\n",
" <td>80.305532</td>\n",
" <td>73.597694</td>\n",
" <td>2.558684</td>\n",
" <td>2.979647</td>\n",
" <td>4.608202</td>\n",
" <td>1002</td>\n",
" <td>3.340</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>2.772466</td>\n",
" <td>0.230337</td>\n",
" <td>34.836234</td>\n",
" <td>173.080063</td>\n",
" <td>310.048857</td>\n",
" <td>2.133865</td>\n",
" <td>3.411067</td>\n",
" <td>4.616444</td>\n",
" <td>8490</td>\n",
" <td>4.130</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.669150</td>\n",
" <td>0.256942</td>\n",
" <td>12.988919</td>\n",
" <td>169.852760</td>\n",
" <td>248.138626</td>\n",
" <td>1.983332</td>\n",
" <td>3.354967</td>\n",
" <td>4.360814</td>\n",
" <td>7104</td>\n",
" <td>5.330</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.361418</td>\n",
" <td>0.088721</td>\n",
" <td>7.141771</td>\n",
" <td>103.810804</td>\n",
" <td>150.728541</td>\n",
" <td>2.151909</td>\n",
" <td>2.570926</td>\n",
" <td>3.628837</td>\n",
" <td>9325</td>\n",
" <td>3.200</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.574249</td>\n",
" <td>0.191095</td>\n",
" <td>5.366988</td>\n",
" <td>141.576605</td>\n",
" <td>358.687607</td>\n",
" <td>2.082324</td>\n",
" <td>3.066174</td>\n",
" <td>4.130323</td>\n",
" <td>2916</td>\n",
" <td>6.850</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>820560</th>\n",
" <td>2.812945</td>\n",
" <td>0.664688</td>\n",
" <td>4.695700</td>\n",
" <td>183.310012</td>\n",
" <td>234.618352</td>\n",
" <td>0.943214</td>\n",
" <td>4.682676</td>\n",
" <td>4.717914</td>\n",
" <td>118</td>\n",
" <td>20.400</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>820561</th>\n",
" <td>2.645238</td>\n",
" <td>0.259376</td>\n",
" <td>12.574937</td>\n",
" <td>1.620020</td>\n",
" <td>339.568072</td>\n",
" <td>1.959126</td>\n",
" <td>3.331350</td>\n",
" <td>4.302346</td>\n",
" <td>15</td>\n",
" <td>17.507</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>820562</th>\n",
" <td>2.373137</td>\n",
" <td>0.202053</td>\n",
" <td>0.732484</td>\n",
" <td>176.499082</td>\n",
" <td>198.026527</td>\n",
" <td>1.893638</td>\n",
" <td>2.852636</td>\n",
" <td>3.655884</td>\n",
" <td>6</td>\n",
" <td>18.071</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>820563</th>\n",
" <td>2.260404</td>\n",
" <td>0.258348</td>\n",
" <td>9.661947</td>\n",
" <td>204.512448</td>\n",
" <td>148.496988</td>\n",
" <td>1.676433</td>\n",
" <td>2.844376</td>\n",
" <td>3.398501</td>\n",
" <td>13</td>\n",
" <td>18.060</td>\n",
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" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>820564</th>\n",
" <td>2.546442</td>\n",
" <td>0.287672</td>\n",
" <td>5.356238</td>\n",
" <td>70.709555</td>\n",
" <td>273.483265</td>\n",
" <td>1.813901</td>\n",
" <td>3.278983</td>\n",
" <td>4.063580</td>\n",
" <td>11</td>\n",
" <td>17.406</td>\n",
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"</table>\n",
"<p>820565 rows × 30 columns</p>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 18
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Label Encoding \"pha\""
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 19,
"source": [
"from sklearn.preprocessing import LabelEncoder\r\n",
"le = LabelEncoder()\r\n",
"df['pha'] = le.fit_transform(df['pha'])\r\n",
"df"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" a e i om w q \\\n",
"0 2.769165 0.076009 10.594067 80.305532 73.597694 2.558684 \n",
"1 2.772466 0.230337 34.836234 173.080063 310.048857 2.133865 \n",
"2 2.669150 0.256942 12.988919 169.852760 248.138626 1.983332 \n",
"3 2.361418 0.088721 7.141771 103.810804 150.728541 2.151909 \n",
"4 2.574249 0.191095 5.366988 141.576605 358.687607 2.082324 \n",
"... ... ... ... ... ... ... \n",
"820560 2.812945 0.664688 4.695700 183.310012 234.618352 0.943214 \n",
"820561 2.645238 0.259376 12.574937 1.620020 339.568072 1.959126 \n",
"820562 2.373137 0.202053 0.732484 176.499082 198.026527 1.893638 \n",
"820563 2.260404 0.258348 9.661947 204.512448 148.496988 1.676433 \n",
"820564 2.546442 0.287672 5.356238 70.709555 273.483265 1.813901 \n",
"\n",
" ad per_y n_obs_used H ... 2 3 4 5 6 \\\n",
"0 2.979647 4.608202 1002 3.340 ... 0.0 0.0 0.0 0.0 0.0 \n",
"1 3.411067 4.616444 8490 4.130 ... 0.0 0.0 0.0 0.0 0.0 \n",
"2 3.354967 4.360814 7104 5.330 ... 0.0 0.0 0.0 0.0 0.0 \n",
"3 2.570926 3.628837 9325 3.200 ... 0.0 0.0 0.0 0.0 0.0 \n",
"4 3.066174 4.130323 2916 6.850 ... 0.0 0.0 0.0 0.0 0.0 \n",
"... ... ... ... ... ... ... ... ... ... ... \n",
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"820562 2.852636 3.655884 6 18.071 ... 0.0 0.0 0.0 0.0 0.0 \n",
"820563 2.844376 3.398501 13 18.060 ... 0.0 0.0 0.0 0.0 0.0 \n",
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" 7 8 9 10 11 \n",
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"\n",
"[820565 rows x 30 columns]"
],
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" <td>2.361418</td>\n",
" <td>0.088721</td>\n",
" <td>7.141771</td>\n",
" <td>103.810804</td>\n",
" <td>150.728541</td>\n",
" <td>2.151909</td>\n",
" <td>2.570926</td>\n",
" <td>3.628837</td>\n",
" <td>9325</td>\n",
" <td>3.200</td>\n",
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" <td>0.191095</td>\n",
" <td>5.366988</td>\n",
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" <td>358.687607</td>\n",
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" <td>148.496988</td>\n",
" <td>1.676433</td>\n",
" <td>2.844376</td>\n",
" <td>3.398501</td>\n",
" <td>13</td>\n",
" <td>18.060</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>820564</th>\n",
" <td>2.546442</td>\n",
" <td>0.287672</td>\n",
" <td>5.356238</td>\n",
" <td>70.709555</td>\n",
" <td>273.483265</td>\n",
" <td>1.813901</td>\n",
" <td>3.278983</td>\n",
" <td>4.063580</td>\n",
" <td>11</td>\n",
" <td>17.406</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>820565 rows × 30 columns</p>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 19
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Checking if everything is consistent"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 20,
"source": [
"df.info()"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 820565 entries, 0 to 820564\n",
"Data columns (total 30 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 a 820565 non-null float64\n",
" 1 e 820565 non-null float64\n",
" 2 i 820565 non-null float64\n",
" 3 om 820565 non-null float64\n",
" 4 w 820565 non-null float64\n",
" 5 q 820565 non-null float64\n",
" 6 ad 820565 non-null float64\n",
" 7 per_y 820565 non-null float64\n",
" 8 n_obs_used 820565 non-null int64 \n",
" 9 H 820565 non-null float64\n",
" 10 pha 820565 non-null int32 \n",
" 11 moid 820565 non-null float64\n",
" 12 n 820565 non-null float64\n",
" 13 per 820565 non-null float64\n",
" 14 ma 820565 non-null float64\n",
" 15 Condition_Code 820565 non-null int64 \n",
" 16 0 820565 non-null float64\n",
" 17 1 820565 non-null float64\n",
" 18 0 820565 non-null float64\n",
" 19 1 820565 non-null float64\n",
" 20 2 820565 non-null float64\n",
" 21 3 820565 non-null float64\n",
" 22 4 820565 non-null float64\n",
" 23 5 820565 non-null float64\n",
" 24 6 820565 non-null float64\n",
" 25 7 820565 non-null float64\n",
" 26 8 820565 non-null float64\n",
" 27 9 820565 non-null float64\n",
" 28 10 820565 non-null float64\n",
" 29 11 820565 non-null float64\n",
"dtypes: float64(27), int32(1), int64(2)\n",
"memory usage: 184.7 MB\n"
]
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Turning \"pha\" from int32 to int64"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 21,
"source": [
"# Done here to prevent future deviation\r\n",
"pha = []\r\n",
"for i in df.pha:\r\n",
" pha.append(int(i))\r\n",
"pha_df = pd.DataFrame(data = pha, columns = ['Pha'])\r\n",
"df.drop(['pha'],axis=1,inplace=True)\r\n",
"df = pd.concat([df,pha_df],axis=1)\r\n",
"df.info()"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 820565 entries, 0 to 820564\n",
"Data columns (total 30 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 a 820565 non-null float64\n",
" 1 e 820565 non-null float64\n",
" 2 i 820565 non-null float64\n",
" 3 om 820565 non-null float64\n",
" 4 w 820565 non-null float64\n",
" 5 q 820565 non-null float64\n",
" 6 ad 820565 non-null float64\n",
" 7 per_y 820565 non-null float64\n",
" 8 n_obs_used 820565 non-null int64 \n",
" 9 H 820565 non-null float64\n",
" 10 moid 820565 non-null float64\n",
" 11 n 820565 non-null float64\n",
" 12 per 820565 non-null float64\n",
" 13 ma 820565 non-null float64\n",
" 14 Condition_Code 820565 non-null int64 \n",
" 15 0 820565 non-null float64\n",
" 16 1 820565 non-null float64\n",
" 17 0 820565 non-null float64\n",
" 18 1 820565 non-null float64\n",
" 19 2 820565 non-null float64\n",
" 20 3 820565 non-null float64\n",
" 21 4 820565 non-null float64\n",
" 22 5 820565 non-null float64\n",
" 23 6 820565 non-null float64\n",
" 24 7 820565 non-null float64\n",
" 25 8 820565 non-null float64\n",
" 26 9 820565 non-null float64\n",
" 27 10 820565 non-null float64\n",
" 28 11 820565 non-null float64\n",
" 29 Pha 820565 non-null int64 \n",
"dtypes: float64(27), int64(3)\n",
"memory usage: 187.8 MB\n"
]
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Making a Heat Map"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 22,
"source": [
"heatmap_df = df[['a', 'e', 'i', 'om', 'w', 'q', 'ad', 'per_y', \r\n",
" 'n_obs_used', 'H', 'moid', 'n', 'per', 'ma',\r\n",
" 'Condition_Code', 'Pha']]"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 23,
"source": [
"plt.figure(figsize=(30,30))\r\n",
"sns.heatmap(heatmap_df.corr().round(2),annot = True)"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"metadata": {},
"execution_count": 23
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 2160x2160 with 2 Axes>"
],
"image/png": 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},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {
"scrolled": true
}
},
{
"cell_type": "markdown",
"source": [
"### Dropping Features that have no relation with 'Pha'"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 24,
"source": [
"df.drop(['om', 'w', 'ad', 'per_y', 'n_obs_used', 'per', 'ma'],axis=1,inplace=True)\r\n",
"df.info()"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 820565 entries, 0 to 820564\n",
"Data columns (total 23 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 a 820565 non-null float64\n",
" 1 e 820565 non-null float64\n",
" 2 i 820565 non-null float64\n",
" 3 q 820565 non-null float64\n",
" 4 H 820565 non-null float64\n",
" 5 moid 820565 non-null float64\n",
" 6 n 820565 non-null float64\n",
" 7 Condition_Code 820565 non-null int64 \n",
" 8 0 820565 non-null float64\n",
" 9 1 820565 non-null float64\n",
" 10 0 820565 non-null float64\n",
" 11 1 820565 non-null float64\n",
" 12 2 820565 non-null float64\n",
" 13 3 820565 non-null float64\n",
" 14 4 820565 non-null float64\n",
" 15 5 820565 non-null float64\n",
" 16 6 820565 non-null float64\n",
" 17 7 820565 non-null float64\n",
" 18 8 820565 non-null float64\n",
" 19 9 820565 non-null float64\n",
" 20 10 820565 non-null float64\n",
" 21 11 820565 non-null float64\n",
" 22 Pha 820565 non-null int64 \n",
"dtypes: float64(21), int64(2)\n",
"memory usage: 144.0 MB\n"
]
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Renaming the columns, due to duplicate names"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 25,
"source": [
"df.columns = ['a', 'e', 'i', 'q', 'H', 'moid', 'n',\r\n",
" 'Condition_Code', 'True', 'False', 0, 1, 2, 3,\r\n",
" 4, 5, 6, 7, 8, 9, 10, 11, 'Pha']"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Creating a function to split a df into testing and training sets"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 26,
"source": [
"'''\r\n",
"TAKES:\r\n",
"@ df --> the dataframe that needs splitting\r\n",
"@ sample_size --> the size of data that the training set will contain\r\n",
"@ maj_percent --> the percentage of the majority\r\n",
"@ min_percent --> the percentage of the minority\r\n",
"RETURNS:\r\n",
"$ df_testing --> data frame containg the data for testing\r\n",
"$ df_training --> data frame containg the data for training\r\n",
"'''\r\n",
"def split_df(df, sample_size, maj_percent, min_percent):\r\n",
" # Splitting into minority, majority\r\n",
" df_minority = df[df['Pha'] == 1]\r\n",
" df_majority = df[df['Pha'] == 0]\r\n",
" df_majority = df_majority.sample(n = 8000)\r\n",
"\r\n",
" # Sampling minority and majority\r\n",
" majority = (sample_size//100)*maj_percent\r\n",
" minority = (sample_size//100)*min_percent\r\n",
" df_majority_subset = df_majority.sample(n = majority)\r\n",
" df_minority_subset = df_minority.sample(n = minority)\r\n",
" # To create the training dataset\r\n",
" df_training = pd.concat([df_minority_subset, df_majority_subset], axis = 0)\r\n",
" df_testing = pd.concat([df_minority, df_majority], axis = 0)\r\n",
"\r\n",
" reindex(df_training)\r\n",
" reindex(df_testing)\r\n",
" \r\n",
" return df_testing, df_training"
],
"outputs": [],
"metadata": {
"scrolled": false
}
},
{
"cell_type": "markdown",
"source": [
"# **Splitting X & y**"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Creating the testing and training dataset"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 27,
"source": [
"df_testing,df_training = split_df(df, 2200, 60, 40)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Splitting for training"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 28,
"source": [
"X_train = df_training.iloc[:, df_training.columns != 'Pha'].values\r\n",
"y_train = df_training.iloc[:, df_training.columns == 'Pha'].values"
],
"outputs": [],
"metadata": {
"scrolled": true
}
},
{
"cell_type": "markdown",
"source": [
"## Splitting for testing"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 29,
"source": [
"X_test = df_testing.iloc[:, df.columns != 'Pha'].values\r\n",
"y_test = df_testing.iloc[:, df.columns == 'Pha'].values"
],
"outputs": [],
"metadata": {
"scrolled": true
}
},
{
"cell_type": "markdown",
"source": [
"# **Scaling**"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Applying Scaling "
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 30,
"source": [
"from sklearn.preprocessing import RobustScaler\r\n",
"scaler = RobustScaler()\r\n",
"X_train_scaled = scaler.fit_transform(X_train)\r\n",
"X_test_scaled = scaler.transform(X_test)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Visualising the Scaled X and Y"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"#### Before Scaling"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 31,
"source": [
"plt.boxplot(X_test[22])\r\n",
"plt.show()"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": "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"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"#### After Scaling"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 32,
"source": [
"plt.boxplot(X_test_scaled[22])\r\n",
"plt.show()"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": "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"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"# **The Models**"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Creating a function to print the accuracy of a model"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 33,
"source": [
"'''\r\n",
"TAKES:\r\n",
"@ model --> The model whose accuracy you want to print\r\n",
"OUT:\r\n",
"$ Prints out the accuracy of the training and testing models\r\n",
"'''\r\n",
"def print_accuracy(model,scaled_model):\r\n",
" accuracy = model.score(X_train,y_train)\r\n",
" print('Model Training Accuracy: {:.3f}'.format(accuracy))\r\n",
" accuracy = model.score(X_test,y_test)\r\n",
" print('Model Testing Accuracy: {:.3f}'.format(accuracy))\r\n",
" print(\"---\" * 8)\r\n",
" accuracy = scaled_model.score(X_train_scaled,y_train)\r\n",
" print('Scaled Model Training Accuracy: {:.3f}'.format(accuracy))\r\n",
" accuracy = scaled_model.score(X_test_scaled,y_test)\r\n",
" print('Scaled Model Testing Accuracy: {:.3f}'.format(accuracy))"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Creating a function to plot the results from the tuning\n"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 34,
"source": [
"'''\r\n",
"TAKES:\r\n",
"@ search --> The hyperparameter tuning object that you want to visualise\r\n",
"OUT:\r\n",
"$ Prints out various information about the accuracy of the model at the different stages of the tunnig process\r\n",
"'''\r\n",
" # Taken from\r\n",
" # https://scikit-learn.org/0.24/auto_examples/model_selection/plot_grid_search_stats.html\r\n",
"def plot_tuning(search):\r\n",
" results_df = pd.DataFrame(search.cv_results_)\r\n",
" results_df = results_df.sort_values(by=['rank_test_score'])\r\n",
" results_df = (\r\n",
" results_df\r\n",
" .set_index(results_df[\"params\"].apply(\r\n",
" lambda x: \"_\".join(str(val) for val in x.values()))\r\n",
" )\r\n",
" .rename_axis('kernel')\r\n",
" )\r\n",
" results_df[\r\n",
" ['params', 'rank_test_score', 'mean_test_score', 'std_test_score']\r\n",
" ]\r\n",
" # create df of model scores ordered by perfomance\r\n",
" model_scores = results_df.filter(regex=r'split\\d*_test_score')\r\n",
" print(model_scores)\r\n",
" # plot 30 examples of dependency between cv fold and AUC scores\r\n",
" fig, ax = plt.subplots()\r\n",
" sns.lineplot(\r\n",
" data=model_scores.transpose().iloc[:30],\r\n",
" dashes=False, palette='Set1', marker='o', alpha=.5, ax=ax\r\n",
" )\r\n",
" ax.set_xlabel(\"CV test fold\", size=12, labelpad=10)\r\n",
" ax.set_ylabel(\"Model AUC\", size=12)\r\n",
" ax.tick_params(bottom=True, labelbottom=False)\r\n",
" plt.legend(bbox_to_anchor=(1.1, 1.1), bbox_transform=ax.transAxes)\r\n",
" plt.show()\r\n",
" # End\r\n",
" # https://scikit-learn.org/0.24/auto_examples/model_selection/plot_grid_search_stats.html"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## ***First Model***"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Creating The Model"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 35,
"source": [
"from sklearn.neighbors import KNeighborsClassifier\r\n",
"knn_model = KNeighborsClassifier()\r\n",
"knn_model_scaled = KNeighborsClassifier()\r\n",
"knn_model.fit(X_train, y_train)\r\n",
"knn_model_scaled.fit(X_train_scaled, y_train)\r\n",
"print_accuracy(knn_model, knn_model_scaled)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model Training Accuracy: 0.990\n",
"Model Testing Accuracy: 0.981\n",
"------------------------\n",
"Scaled Model Training Accuracy: 0.989\n",
"Scaled Model Testing Accuracy: 0.982\n"
]
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Cross Validation"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 36,
"source": [
"from sklearn.model_selection import cross_val_score\r\n",
"score = cross_val_score(knn_model, X_train, y_train, cv=4, scoring=\"accuracy\")\r\n",
"print(score)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[0.98363636 0.98727273 0.97636364 0.98909091]\n"
]
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 37,
"source": [
"print(\"Average Cross Validation Model Accuracy: {:.3f}\".format(score.mean()))"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Average Cross Validation Model Accuracy: 0.984\n"
]
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Model Tuning"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"#### Parameters"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 38,
"source": [
"k_list = list(range(1,10))\n",
"params = dict(n_neighbors = k_list)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"#### Randomised Search"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 39,
"source": [
"from sklearn.model_selection import RandomizedSearchCV\n",
"random_search = RandomizedSearchCV(knn_model, params, cv = 5, scoring = 'accuracy', n_jobs= -1)\n",
"random_search.fit(X_test, y_test)"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"RandomizedSearchCV(cv=5, estimator=KNeighborsClassifier(), n_jobs=-1,\n",
" param_distributions={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8,\n",
" 9]},\n",
" scoring='accuracy')"
]
},
"metadata": {},
"execution_count": 39
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 40,
"source": [
"rs_df = pd.DataFrame(random_search.cv_results_)[['mean_test_score','params']]\n",
"plt.plot(k_list,rs_df.mean_test_score, marker = 'o')\n",
"plt.xlabel('Number of Nearest Neighbours')\n",
"plt.ylabel('Mean CV Scores')\n",
"plt.title('Randomised Search on KNN')\n",
"plt.grid()"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"#### Grid Search"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 41,
"source": [
"from sklearn.model_selection import GridSearchCV\n",
"grid_search = GridSearchCV(knn_model, params, cv = 5, scoring = 'accuracy', n_jobs= -1)\n",
"grid_search.fit(X_test, y_test)"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"GridSearchCV(cv=5, estimator=KNeighborsClassifier(), n_jobs=-1,\n",
" param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9]},\n",
" scoring='accuracy')"
]
},
"metadata": {},
"execution_count": 41
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 42,
"source": [
"rs_df = pd.DataFrame(grid_search.cv_results_)[['mean_test_score','params']]\n",
"plt.plot(k_list,rs_df.mean_test_score, marker = 'o')\n",
"plt.xlabel('Number of Nearest Neighbours')\n",
"plt.ylabel('Mean CV Scores')\n",
"plt.title('Grid Search on KNN')\n",
"plt.grid()"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"#### Tuning Results"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 43,
"source": [
"print(\"Randomised Search\")\n",
"print(\"The Best Accuracy of the Model is: {:.3f}\".format(random_search.best_score_))\n",
"print(\"With Parameters: \", random_search.best_params_)\n",
"print(\"-----\" *5)\n",
"print(\"Grid Search\")\n",
"print(\"The Best Accuracy of the Model is: {:.3f}\".format(grid_search.best_score_))\n",
"print(\"With Parameters: \", grid_search.best_params_)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Randomised Search\n",
"The Best Accuracy of the Model is: 0.986\n",
"With Parameters: {'n_neighbors': 3}\n",
"-------------------------\n",
"Grid Search\n",
"The Best Accuracy of the Model is: 0.986\n",
"With Parameters: {'n_neighbors': 3}\n"
]
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Cross Validation"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 44,
"source": [
"value = list(random_search.best_params_.values())[:2]\n",
"best_knn_model = KNeighborsClassifier(n_neighbors=value[0])\n",
"best_knn_model.fit(X_train_scaled, y_train)\n",
"score = cross_val_score(best_knn_model, X_train, y_train, cv=4, scoring=\"accuracy\")\n",
"print(score)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[0.99454545 0.98727273 0.98 0.99272727]\n"
]
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 45,
"source": [
"print(\"Average Cross Validation Model Accuracy: {:.3f}\".format(score.mean()))"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Average Cross Validation Model Accuracy: 0.989\n"
]
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Receiver Operating Characteristic"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 46,
"source": [
"from sklearn import metrics\n",
"metrics.plot_roc_curve(best_knn_model, X_test_scaled, y_test) \n",
"plt.title('ROC on KNN')\n",
"plt.show()"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Confusion Matrix"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 47,
"source": [
"y_pred = knn_model_scaled.predict(X_test_scaled)\n",
"from sklearn.metrics import confusion_matrix\n",
"cfm = confusion_matrix(y_test, y_pred)\n",
"print(cfm)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[7826 174]\n",
" [ 5 2010]]\n"
]
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 48,
"source": [
"group_names = [\"True Neg\",\"False Pos\",\"False Neg\",\"True Pos\"]\n",
"group_counts = [\"{0:0.0f}\".format(value) for value in cfm.flatten()]\n",
"group_percentages = [\"{0:.2%}\".format(value) for value in cfm.flatten()/np.sum(cfm)]\n",
"labels = [f\"{v1}\\n{v2}\\n{v3}\" for v1, v2, v3 in zip(group_names,group_counts,group_percentages)]\n",
"labels = np.asarray(labels).reshape(2,2)\n",
"df = pd.DataFrame(cfm, [\"Not Dangerous\",\"Dangerous\"], [\"Not Dangerous\",\"Dangerous\"])\n",
"plt.title(\"Confusion Matrix of KNN\", fontsize =16)\n",
"sns.heatmap(df, annot=labels, fmt=\"\", cmap='Blues')"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:title={'center':'Confusion Matrix of KNN'}>"
]
},
"metadata": {},
"execution_count": 48
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
],
"image/png": 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"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Classification Report\n"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 49,
"source": [
"from sklearn.metrics import classification_report\n",
"target_names = ['Not Dangerous', 'Dangerous']\n",
"print(classification_report(y_test, y_pred, target_names=target_names))"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
"Not Dangerous 1.00 0.98 0.99 8000\n",
" Dangerous 0.92 1.00 0.96 2015\n",
"\n",
" accuracy 0.98 10015\n",
" macro avg 0.96 0.99 0.97 10015\n",
" weighted avg 0.98 0.98 0.98 10015\n",
"\n"
]
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## ***Second Model***"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Creating The Model"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 50,
"source": [
"from sklearn.svm import SVC\n",
"svm_model = SVC()\n",
"svm_model_scaled = SVC()\n",
"svm_model.fit(X_train,y_train)\n",
"svm_model_scaled.fit(X_train_scaled, y_train)\n",
"print_accuracy(svm_model, svm_model_scaled)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model Training Accuracy: 0.987\n",
"Model Testing Accuracy: 0.978\n",
"------------------------\n",
"Scaled Model Training Accuracy: 0.989\n",
"Scaled Model Testing Accuracy: 0.981\n"
]
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Cross Validation"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 51,
"source": [
"from sklearn.model_selection import cross_val_score\n",
"score = cross_val_score(svm_model, X_train, y_train, cv=4, scoring=\"accuracy\")\n",
"print(score)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[0.99454545 0.98909091 0.97636364 0.98545455]\n"
]
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 52,
"source": [
"print(\"Average Cross Validation Model Accuracy: {:.3f}\".format(score.mean()))"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Average Cross Validation Model Accuracy: 0.986\n"
]
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Model Tuning"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"#### Parameters"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 53,
"source": [
"C = [10, 1, 0.1]\n",
"kernel = ['linear','rbf']\n",
"params = dict(kernel = kernel, C = C)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"#### Random Search"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 54,
"source": [
"random_search = RandomizedSearchCV(svm_model_scaled, params, cv = 5, scoring = 'accuracy', n_jobs= -1)\n",
"random_search.fit(X_test, y_test)"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"RandomizedSearchCV(cv=5, estimator=SVC(), n_jobs=-1,\n",
" param_distributions={'C': [10, 1, 0.1],\n",
" 'kernel': ['linear', 'rbf']},\n",
" scoring='accuracy')"
]
},
"metadata": {},
"execution_count": 54
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 55,
"source": [
"plot_tuning(random_search)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" split0_test_score split1_test_score split2_test_score \\\n",
"kernel \n",
"linear_10 0.992511 0.994508 0.994508 \n",
"linear_1 0.990015 0.990514 0.992511 \n",
"linear_0.1 0.987019 0.987519 0.993010 \n",
"rbf_10 0.987019 0.987019 0.990514 \n",
"rbf_1 0.981528 0.983525 0.988517 \n",
"rbf_0.1 0.962556 0.975037 0.979531 \n",
"\n",
" split3_test_score split4_test_score \n",
"kernel \n",
"linear_10 0.992012 0.995007 \n",
"linear_1 0.991013 0.994009 \n",
"linear_0.1 0.989016 0.990015 \n",
"rbf_10 0.988517 0.991013 \n",
"rbf_1 0.985522 0.988018 \n",
"rbf_0.1 0.978033 0.980529 \n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"#### Grid Search"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 56,
"source": [
"grid_search = GridSearchCV(svm_model_scaled, params, cv = 5, scoring = 'accuracy', n_jobs= -1)\n",
"grid_search.fit(X_test, y_test)"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"GridSearchCV(cv=5, estimator=SVC(), n_jobs=-1,\n",
" param_grid={'C': [10, 1, 0.1], 'kernel': ['linear', 'rbf']},\n",
" scoring='accuracy')"
]
},
"metadata": {},
"execution_count": 56
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 57,
"source": [
"plot_tuning(grid_search)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" split0_test_score split1_test_score split2_test_score \\\n",
"kernel \n",
"10_linear 0.992511 0.994508 0.994508 \n",
"1_linear 0.990015 0.990514 0.992511 \n",
"0.1_linear 0.987019 0.987519 0.993010 \n",
"10_rbf 0.987019 0.987019 0.990514 \n",
"1_rbf 0.981528 0.983525 0.988517 \n",
"0.1_rbf 0.962556 0.975037 0.979531 \n",
"\n",
" split3_test_score split4_test_score \n",
"kernel \n",
"10_linear 0.992012 0.995007 \n",
"1_linear 0.991013 0.994009 \n",
"0.1_linear 0.989016 0.990015 \n",
"10_rbf 0.988517 0.991013 \n",
"1_rbf 0.985522 0.988018 \n",
"0.1_rbf 0.978033 0.980529 \n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"#### Tuning Results"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 58,
"source": [
"print(\"Randomised Search\")\n",
"print(\"The Best Accuracy of the Model is: {:.3f}\".format(random_search.best_score_))\n",
"print(\"With Parameters: \", random_search.best_params_)\n",
"print(\"-----\" *5)\n",
"print(\"Grid Search\")\n",
"print(\"The Best Accuracy of the Model is: {:.3f}\".format(grid_search.best_score_))\n",
"print(\"With Parameters: \", grid_search.best_params_)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Randomised Search\n",
"The Best Accuracy of the Model is: 0.994\n",
"With Parameters: {'kernel': 'linear', 'C': 10}\n",
"-------------------------\n",
"Grid Search\n",
"The Best Accuracy of the Model is: 0.994\n",
"With Parameters: {'C': 10, 'kernel': 'linear'}\n"
]
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Cross Validation"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 59,
"source": [
"values = list(grid_search.best_params_.values())\n",
"best_svm_model = SVC(C = values[0], kernel = values[1])\n",
"best_svm_model.fit(X_train_scaled, y_train)\n",
"score = cross_val_score(best_svm_model, X_train, y_train, cv=4, scoring=\"accuracy\")\n",
"print(score)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[0.99454545 0.99272727 0.98181818 0.99636364]\n"
]
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 60,
"source": [
"print(\"Average Cross Validation Model Accuracy: {:.3f}\".format(score.mean()))"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Average Cross Validation Model Accuracy: 0.991\n"
]
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Receiver Operating Characteristic"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 61,
"source": [
"from sklearn import metrics\n",
"metrics.plot_roc_curve(best_svm_model, X_test_scaled, y_test) \n",
"plt.title('ROC on SVM')\n",
"plt.show()"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Confusion Matrix"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 62,
"source": [
"y_pred = svm_model_scaled.predict(X_test_scaled)\n",
"cfm = confusion_matrix(y_test, y_pred)\n",
"print(cfm)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[7809 191]\n",
" [ 2 2013]]\n"
]
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 63,
"source": [
"group_names = [\"True Neg\",\"False Pos\",\"False Neg\",\"True Pos\"]\n",
"group_counts = [\"{0:0.0f}\".format(value) for value in cfm.flatten()]\n",
"group_percentages = [\"{0:.2%}\".format(value) for value in cfm.flatten()/np.sum(cfm)]\n",
"labels = [f\"{v1}\\n{v2}\\n{v3}\" for v1, v2, v3 in zip(group_names,group_counts,group_percentages)]\n",
"labels = np.asarray(labels).reshape(2,2)\n",
"df = pd.DataFrame(cfm, [\"Not Dangerous\",\"Dangerous\"], [\"Not Dangerous\",\"Dangerous\"])\n",
"plt.title(\"Confusion Matrix of S.V.M.\", fontsize =16)\n",
"sns.heatmap(df, annot=labels, fmt=\"\", cmap='Blues')"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:title={'center':'Confusion Matrix of S.V.M.'}>"
]
},
"metadata": {},
"execution_count": 63
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
],
"image/png": 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"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Classification Report"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 64,
"source": [
"from sklearn.metrics import classification_report\n",
"target_names = ['Not Dangerous', 'Dangerous']\n",
"print(classification_report(y_test, y_pred, target_names=target_names))"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
"Not Dangerous 1.00 0.98 0.99 8000\n",
" Dangerous 0.91 1.00 0.95 2015\n",
"\n",
" accuracy 0.98 10015\n",
" macro avg 0.96 0.99 0.97 10015\n",
" weighted avg 0.98 0.98 0.98 10015\n",
"\n"
]
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## ***Third Model***"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Creating The Model"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 65,
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"lr_model = LogisticRegression()\n",
"lr_model_scaled = LogisticRegression()\n",
"lr_model.fit(X_train,y_train)\n",
"lr_model_scaled.fit(X_train_scaled,y_train)\n",
"print_accuracy(lr_model, lr_model_scaled)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model Training Accuracy: 0.990\n",
"Model Testing Accuracy: 0.985\n",
"------------------------\n",
"Scaled Model Training Accuracy: 0.989\n",
"Scaled Model Testing Accuracy: 0.984\n"
]
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Cross Validation"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 66,
"source": [
"from sklearn.model_selection import cross_val_score\n",
"score = cross_val_score(lr_model, X_train, y_train, cv=4, scoring=\"accuracy\")\n",
"print(score)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[0.99454545 0.98909091 0.97636364 0.98909091]\n"
]
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 67,
"source": [
"print(\"Average Cross Validation Model Accuracy: {:.3f}\".format(score.mean()))"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Average Cross Validation Model Accuracy: 0.987\n"
]
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Model Tuning"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"#### Parameters"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 68,
"source": [
"penalty = ['l1', 'l2']\n",
"solver = ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']\n",
"params = dict(penalty = penalty, C = C, solver = solver)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"#### Random Search"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 69,
"source": [
"random_search = RandomizedSearchCV(lr_model, params, cv = 5, scoring = 'accuracy', n_jobs= -1)\n",
"random_search.fit(X_test, y_test)"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_jobs=-1,\n",
" param_distributions={'C': [10, 1, 0.1],\n",
" 'penalty': ['l1', 'l2'],\n",
" 'solver': ['newton-cg', 'lbfgs',\n",
" 'liblinear', 'sag',\n",
" 'saga']},\n",
" scoring='accuracy')"
]
},
"metadata": {},
"execution_count": 69
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 70,
"source": [
"plot_tuning(random_search)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" split0_test_score split1_test_score split2_test_score \\\n",
"kernel \n",
"liblinear_l1_1 0.993010 0.994009 0.995007 \n",
"newton-cg_l2_10 0.991513 0.993010 0.994508 \n",
"newton-cg_l2_1 0.988018 0.989516 0.992012 \n",
"liblinear_l1_0.1 0.981028 0.981028 0.984024 \n",
"sag_l2_10 0.981528 0.981028 0.983025 \n",
"saga_l2_10 0.981028 0.979531 0.983525 \n",
"saga_l1_1 0.980529 0.979531 0.983525 \n",
"saga_l2_0.1 0.980030 0.979531 0.983025 \n",
"newton-cg_l1_0.1 NaN NaN NaN \n",
"sag_l1_0.1 NaN NaN NaN \n",
"\n",
" split3_test_score split4_test_score \n",
"kernel \n",
"liblinear_l1_1 0.993010 0.995007 \n",
"newton-cg_l2_10 0.992511 0.994009 \n",
"newton-cg_l2_1 0.990015 0.990514 \n",
"liblinear_l1_0.1 0.982526 0.987019 \n",
"sag_l2_10 0.979531 0.979531 \n",
"saga_l2_10 0.979531 0.980529 \n",
"saga_l1_1 0.979531 0.980529 \n",
"saga_l2_0.1 0.979531 0.980529 \n",
"newton-cg_l1_0.1 NaN NaN \n",
"sag_l1_0.1 NaN NaN \n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"#### Grid Search"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 71,
"source": [
"grid_search = GridSearchCV(lr_model, params, cv = 5, scoring = 'accuracy', n_jobs= -1)\n",
"grid_search.fit(X_test, y_test)"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"GridSearchCV(cv=5, estimator=LogisticRegression(), n_jobs=-1,\n",
" param_grid={'C': [10, 1, 0.1], 'penalty': ['l1', 'l2'],\n",
" 'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag',\n",
" 'saga']},\n",
" scoring='accuracy')"
]
},
"metadata": {},
"execution_count": 71
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 72,
"source": [
"plot_tuning(grid_search)\n"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" split0_test_score split1_test_score split2_test_score \\\n",
"kernel \n",
"10_l1_liblinear 0.995007 0.995507 0.996006 \n",
"1_l1_liblinear 0.993010 0.994009 0.995007 \n",
"10_l2_newton-cg 0.991513 0.993010 0.994508 \n",
"10_l2_liblinear 0.991513 0.992012 0.994009 \n",
"10_l2_lbfgs 0.992012 0.990514 0.994508 \n",
"1_l2_newton-cg 0.988018 0.989516 0.992012 \n",
"1_l2_lbfgs 0.987519 0.989016 0.990015 \n",
"1_l2_liblinear 0.987519 0.987519 0.989516 \n",
"0.1_l2_newton-cg 0.986520 0.987019 0.988517 \n",
"0.1_l2_lbfgs 0.986520 0.987019 0.988018 \n",
"0.1_l1_liblinear 0.981028 0.981028 0.984024 \n",
"0.1_l2_liblinear 0.981528 0.981028 0.984024 \n",
"0.1_l2_sag 0.981528 0.980529 0.983025 \n",
"1_l2_sag 0.981528 0.981028 0.983025 \n",
"10_l2_sag 0.981528 0.981028 0.983025 \n",
"10_l2_saga 0.981028 0.979531 0.983525 \n",
"10_l1_saga 0.981028 0.979531 0.983525 \n",
"1_l2_saga 0.980529 0.979531 0.983525 \n",
"1_l1_saga 0.980529 0.979531 0.983525 \n",
"0.1_l2_saga 0.980030 0.979531 0.983025 \n",
"0.1_l1_saga 0.980030 0.979031 0.983025 \n",
"0.1_l1_newton-cg NaN NaN NaN \n",
"0.1_l1_lbfgs NaN NaN NaN \n",
"1_l1_lbfgs NaN NaN NaN \n",
"0.1_l1_sag NaN NaN NaN \n",
"1_l1_newton-cg NaN NaN NaN \n",
"10_l1_sag NaN NaN NaN \n",
"10_l1_lbfgs NaN NaN NaN \n",
"1_l1_sag NaN NaN NaN \n",
"10_l1_newton-cg NaN NaN NaN \n",
"\n",
" split3_test_score split4_test_score \n",
"kernel \n",
"10_l1_liblinear 0.994508 0.993510 \n",
"1_l1_liblinear 0.993010 0.995007 \n",
"10_l2_newton-cg 0.992511 0.994009 \n",
"10_l2_liblinear 0.992511 0.993510 \n",
"10_l2_lbfgs 0.991513 0.992511 \n",
"1_l2_newton-cg 0.990015 0.990514 \n",
"1_l2_lbfgs 0.989516 0.991013 \n",
"1_l2_liblinear 0.988018 0.990514 \n",
"0.1_l2_newton-cg 0.986021 0.989016 \n",
"0.1_l2_lbfgs 0.985022 0.989016 \n",
"0.1_l1_liblinear 0.982526 0.987019 \n",
"0.1_l2_liblinear 0.980529 0.983525 \n",
"0.1_l2_sag 0.980030 0.980529 \n",
"1_l2_sag 0.979531 0.979531 \n",
"10_l2_sag 0.979531 0.979531 \n",
"10_l2_saga 0.979531 0.980529 \n",
"10_l1_saga 0.979531 0.980529 \n",
"1_l2_saga 0.979531 0.980529 \n",
"1_l1_saga 0.979531 0.980529 \n",
"0.1_l2_saga 0.979531 0.980529 \n",
"0.1_l1_saga 0.979031 0.981028 \n",
"0.1_l1_newton-cg NaN NaN \n",
"0.1_l1_lbfgs NaN NaN \n",
"1_l1_lbfgs NaN NaN \n",
"0.1_l1_sag NaN NaN \n",
"1_l1_newton-cg NaN NaN \n",
"10_l1_sag NaN NaN \n",
"10_l1_lbfgs NaN NaN \n",
"1_l1_sag NaN NaN \n",
"10_l1_newton-cg NaN NaN \n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"#### Tuning Results"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 73,
"source": [
"print(\"Randomised Search\")\n",
"print(\"The Best Accuracy of the Model is: {:.3f}\".format(random_search.best_score_))\n",
"print(\"With Parameters: \", random_search.best_params_)\n",
"print(\"-----\" *5)\n",
"print(\"Grid Search\")\n",
"print(\"The Best Accuracy of the Model is: {:.3f}\".format(grid_search.best_score_))\n",
"print(\"With Parameters: \", grid_search.best_params_)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Randomised Search\n",
"The Best Accuracy of the Model is: 0.994\n",
"With Parameters: {'solver': 'liblinear', 'penalty': 'l1', 'C': 1}\n",
"-------------------------\n",
"Grid Search\n",
"The Best Accuracy of the Model is: 0.995\n",
"With Parameters: {'C': 10, 'penalty': 'l1', 'solver': 'liblinear'}\n"
]
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Cross Validation"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 74,
"source": [
"values = list(grid_search.best_params_.values())\n",
"print(values)\n",
"best_lr_model= LogisticRegression(C = values[0], penalty = values[1], solver = values[2])\n",
"best_lr_model.fit(X_train,y_train)\n",
"score = cross_val_score(best_lr_model, X_train, y_train, cv=4, scoring=\"accuracy\")\n",
"print(score)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[10, 'l1', 'liblinear']\n",
"[0.99818182 0.99454545 0.98909091 0.99272727]\n"
]
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 75,
"source": [
"print(\"Average Cross Validation Model Accuracy: {:.3f}\".format(score.mean()))"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Average Cross Validation Model Accuracy: 0.994\n"
]
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Receiver Operating Characteristic"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 76,
"source": [
"from sklearn import metrics\n",
"metrics.plot_roc_curve(best_lr_model, X_test, y_test)\n",
"plt.title('ROC on Linear Regression') \n",
"plt.show()"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Confusion Matrix"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 77,
"source": [
"y_pred = lr_model_scaled.predict(X_test_scaled)\n",
"cfm = confusion_matrix(y_test, y_pred)\n",
"print(cfm)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[7842 158]\n",
" [ 5 2010]]\n"
]
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 78,
"source": [
"group_names = [\"True Neg\",\"False Pos\",\"False Neg\",\"True Pos\"]\n",
"group_counts = [\"{0:0.0f}\".format(value) for value in cfm.flatten()]\n",
"group_percentages = [\"{0:.2%}\".format(value) for value in cfm.flatten()/np.sum(cfm)]\n",
"labels = [f\"{v1}\\n{v2}\\n{v3}\" for v1, v2, v3 in zip(group_names,group_counts,group_percentages)]\n",
"labels = np.asarray(labels).reshape(2,2)\n",
"df = pd.DataFrame(cfm, [\"Not Dangerous\",\"Dangerous\"], [\"Not Dangerous\",\"Dangerous\"])\n",
"plt.title(\"Confusion Matrix of Linear Regression\", fontsize =16)\n",
"sns.heatmap(df, annot=labels, fmt=\"\", cmap='Blues')"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:title={'center':'Confusion Matrix of Linear Regression'}>"
]
},
"metadata": {},
"execution_count": 78
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
],
"image/png": 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"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Classification Report"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 79,
"source": [
"from sklearn.metrics import classification_report\n",
"target_names = ['Not Dangerous', 'Dangerous']\n",
"print(classification_report(y_test, y_pred, target_names=target_names))\n"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
"Not Dangerous 1.00 0.98 0.99 8000\n",
" Dangerous 0.93 1.00 0.96 2015\n",
"\n",
" accuracy 0.98 10015\n",
" macro avg 0.96 0.99 0.98 10015\n",
" weighted avg 0.98 0.98 0.98 10015\n",
"\n"
]
}
],
"metadata": {}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}