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projjal1/which-character-are-you.ipynb

Created November 16, 2020 04:48
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{
"nbformat": 4,
"nbformat_minor": 0,
"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": "3.7.3"
},
"colab": {
"name": "Which Character are you?.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/projjal1/b60de169b1d46ff1a6533b25b5b2b0af/which-character-are-you.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "0LbMhPWCzcyM"
},
"source": [
"import pandas as pd"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "12JX87D3zcyV"
},
"source": [
"data=pd.read_csv('dataset.csv')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "PXh1DZ87zcyY",
"outputId": "f61a638c-df81-4a87-f426-1e5e48dd9c47",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 244
}
},
"source": [
"#lets look at few rows\n",
"data.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <th>BAP37</th>\n",
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" <th>BAP39</th>\n",
" <th>...</th>\n",
" <th>BAP229</th>\n",
" <th>BAP230</th>\n",
" <th>BAP231</th>\n",
" <th>BAP232</th>\n",
" <th>BAP233</th>\n",
" <th>BAP234</th>\n",
" <th>BAP235</th>\n",
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" <th>BAP238</th>\n",
" <th>BAP239</th>\n",
" <th>BAP240</th>\n",
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" <th>BAP242</th>\n",
" <th>BAP243</th>\n",
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" <th>BAP245</th>\n",
" <th>BAP246</th>\n",
" <th>BAP247</th>\n",
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" <th>BAP251</th>\n",
" <th>BAP252</th>\n",
" <th>BAP253</th>\n",
" <th>BAP254</th>\n",
" <th>BAP255</th>\n",
" <th>BAP256</th>\n",
" <th>BAP257</th>\n",
" <th>BAP258</th>\n",
" <th>BAP259</th>\n",
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" <th>BAP263</th>\n",
" <th>BAP264</th>\n",
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" <th>BAP268</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NCIS/1</td>\n",
" <td>86.5</td>\n",
" <td>82.1</td>\n",
" <td>74.8</td>\n",
" <td>7.9</td>\n",
" <td>31.1</td>\n",
" <td>78.1</td>\n",
" <td>37.9</td>\n",
" <td>20.6</td>\n",
" <td>55.7</td>\n",
" <td>67.3</td>\n",
" <td>83.7</td>\n",
" <td>78.6</td>\n",
" <td>15.1</td>\n",
" <td>83.0</td>\n",
" <td>17.7</td>\n",
" <td>43.1</td>\n",
" <td>48.0</td>\n",
" <td>20.1</td>\n",
" <td>23.4</td>\n",
" <td>85.5</td>\n",
" <td>49.6</td>\n",
" <td>27.2</td>\n",
" <td>13.9</td>\n",
" <td>8.5</td>\n",
" <td>52.6</td>\n",
" <td>10.9</td>\n",
" <td>60.0</td>\n",
" <td>7.2</td>\n",
" <td>72.6</td>\n",
" <td>44.6</td>\n",
" <td>63.2</td>\n",
" <td>5.6</td>\n",
" <td>65.4</td>\n",
" <td>91.6</td>\n",
" <td>84.0</td>\n",
" <td>58.0</td>\n",
" <td>83.5</td>\n",
" <td>63.0</td>\n",
" <td>12.5</td>\n",
" <td>...</td>\n",
" <td>24.8</td>\n",
" <td>14.1</td>\n",
" <td>21.7</td>\n",
" <td>25.5</td>\n",
" <td>15.1</td>\n",
" <td>88.3</td>\n",
" <td>54.7</td>\n",
" <td>37.9</td>\n",
" <td>44.8</td>\n",
" <td>51.5</td>\n",
" <td>81.1</td>\n",
" <td>30.3</td>\n",
" <td>44.8</td>\n",
" <td>35.4</td>\n",
" <td>80.8</td>\n",
" <td>65.7</td>\n",
" <td>65.3</td>\n",
" <td>65.8</td>\n",
" <td>37.6</td>\n",
" <td>57.1</td>\n",
" <td>37.1</td>\n",
" <td>19.6</td>\n",
" <td>32.6</td>\n",
" <td>24.3</td>\n",
" <td>47.8</td>\n",
" <td>41.5</td>\n",
" <td>51.5</td>\n",
" <td>61.6</td>\n",
" <td>61.8</td>\n",
" <td>38.5</td>\n",
" <td>38.4</td>\n",
" <td>57.9</td>\n",
" <td>21.1</td>\n",
" <td>64.2</td>\n",
" <td>23.4</td>\n",
" <td>29.4</td>\n",
" <td>76.7</td>\n",
" <td>32.8</td>\n",
" <td>81.9</td>\n",
" <td>11.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NCIS/2</td>\n",
" <td>18.5</td>\n",
" <td>88.2</td>\n",
" <td>24.3</td>\n",
" <td>14.4</td>\n",
" <td>13.1</td>\n",
" <td>40.4</td>\n",
" <td>69.7</td>\n",
" <td>73.3</td>\n",
" <td>48.7</td>\n",
" <td>55.0</td>\n",
" <td>79.0</td>\n",
" <td>46.7</td>\n",
" <td>70.9</td>\n",
" <td>51.0</td>\n",
" <td>68.6</td>\n",
" <td>47.5</td>\n",
" <td>24.5</td>\n",
" <td>64.6</td>\n",
" <td>19.6</td>\n",
" <td>74.0</td>\n",
" <td>58.3</td>\n",
" <td>31.2</td>\n",
" <td>76.5</td>\n",
" <td>27.9</td>\n",
" <td>47.5</td>\n",
" <td>62.5</td>\n",
" <td>26.7</td>\n",
" <td>12.8</td>\n",
" <td>43.3</td>\n",
" <td>25.4</td>\n",
" <td>52.9</td>\n",
" <td>54.0</td>\n",
" <td>20.2</td>\n",
" <td>15.0</td>\n",
" <td>44.4</td>\n",
" <td>39.0</td>\n",
" <td>49.5</td>\n",
" <td>28.3</td>\n",
" <td>17.0</td>\n",
" <td>...</td>\n",
" <td>67.8</td>\n",
" <td>38.6</td>\n",
" <td>53.7</td>\n",
" <td>58.1</td>\n",
" <td>62.9</td>\n",
" <td>75.6</td>\n",
" <td>35.5</td>\n",
" <td>72.2</td>\n",
" <td>14.0</td>\n",
" <td>81.4</td>\n",
" <td>38.2</td>\n",
" <td>72.8</td>\n",
" <td>55.1</td>\n",
" <td>71.4</td>\n",
" <td>74.8</td>\n",
" <td>25.6</td>\n",
" <td>23.9</td>\n",
" <td>57.7</td>\n",
" <td>78.0</td>\n",
" <td>36.9</td>\n",
" <td>41.0</td>\n",
" <td>43.1</td>\n",
" <td>56.4</td>\n",
" <td>50.6</td>\n",
" <td>67.0</td>\n",
" <td>57.8</td>\n",
" <td>50.8</td>\n",
" <td>19.9</td>\n",
" <td>60.1</td>\n",
" <td>38.1</td>\n",
" <td>54.8</td>\n",
" <td>83.3</td>\n",
" <td>84.5</td>\n",
" <td>32.1</td>\n",
" <td>42.1</td>\n",
" <td>64.7</td>\n",
" <td>69.2</td>\n",
" <td>34.3</td>\n",
" <td>70.4</td>\n",
" <td>34.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NCIS/3</td>\n",
" <td>16.0</td>\n",
" <td>86.3</td>\n",
" <td>11.9</td>\n",
" <td>64.8</td>\n",
" <td>35.9</td>\n",
" <td>61.5</td>\n",
" <td>15.5</td>\n",
" <td>70.2</td>\n",
" <td>60.2</td>\n",
" <td>22.8</td>\n",
" <td>55.1</td>\n",
" <td>68.6</td>\n",
" <td>90.4</td>\n",
" <td>62.2</td>\n",
" <td>67.0</td>\n",
" <td>86.1</td>\n",
" <td>66.1</td>\n",
" <td>55.6</td>\n",
" <td>30.5</td>\n",
" <td>36.8</td>\n",
" <td>67.2</td>\n",
" <td>61.3</td>\n",
" <td>78.1</td>\n",
" <td>29.4</td>\n",
" <td>27.9</td>\n",
" <td>22.4</td>\n",
" <td>35.1</td>\n",
" <td>6.8</td>\n",
" <td>8.7</td>\n",
" <td>11.6</td>\n",
" <td>79.0</td>\n",
" <td>9.6</td>\n",
" <td>45.1</td>\n",
" <td>9.0</td>\n",
" <td>52.9</td>\n",
" <td>45.7</td>\n",
" <td>91.2</td>\n",
" <td>74.3</td>\n",
" <td>15.4</td>\n",
" <td>...</td>\n",
" <td>78.9</td>\n",
" <td>28.5</td>\n",
" <td>17.8</td>\n",
" <td>19.0</td>\n",
" <td>67.5</td>\n",
" <td>92.0</td>\n",
" <td>30.4</td>\n",
" <td>81.4</td>\n",
" <td>33.7</td>\n",
" <td>85.1</td>\n",
" <td>72.7</td>\n",
" <td>18.4</td>\n",
" <td>78.6</td>\n",
" <td>71.8</td>\n",
" <td>93.9</td>\n",
" <td>91.0</td>\n",
" <td>15.9</td>\n",
" <td>24.2</td>\n",
" <td>37.1</td>\n",
" <td>54.1</td>\n",
" <td>35.6</td>\n",
" <td>42.9</td>\n",
" <td>42.4</td>\n",
" <td>91.0</td>\n",
" <td>63.8</td>\n",
" <td>40.8</td>\n",
" <td>14.9</td>\n",
" <td>22.6</td>\n",
" <td>27.8</td>\n",
" <td>52.7</td>\n",
" <td>18.9</td>\n",
" <td>75.2</td>\n",
" <td>93.7</td>\n",
" <td>49.6</td>\n",
" <td>36.0</td>\n",
" <td>31.5</td>\n",
" <td>22.3</td>\n",
" <td>15.9</td>\n",
" <td>78.8</td>\n",
" <td>13.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NCIS/4</td>\n",
" <td>63.5</td>\n",
" <td>64.7</td>\n",
" <td>29.1</td>\n",
" <td>34.1</td>\n",
" <td>24.5</td>\n",
" <td>84.2</td>\n",
" <td>7.1</td>\n",
" <td>49.3</td>\n",
" <td>14.1</td>\n",
" <td>33.1</td>\n",
" <td>70.3</td>\n",
" <td>88.1</td>\n",
" <td>46.5</td>\n",
" <td>79.4</td>\n",
" <td>15.6</td>\n",
" <td>55.4</td>\n",
" <td>82.9</td>\n",
" <td>63.7</td>\n",
" <td>59.7</td>\n",
" <td>63.1</td>\n",
" <td>72.5</td>\n",
" <td>74.3</td>\n",
" <td>23.8</td>\n",
" <td>46.3</td>\n",
" <td>17.9</td>\n",
" <td>9.9</td>\n",
" <td>84.3</td>\n",
" <td>6.7</td>\n",
" <td>61.2</td>\n",
" <td>21.6</td>\n",
" <td>88.2</td>\n",
" <td>7.2</td>\n",
" <td>78.8</td>\n",
" <td>32.0</td>\n",
" <td>84.0</td>\n",
" <td>78.8</td>\n",
" <td>90.4</td>\n",
" <td>77.5</td>\n",
" <td>15.0</td>\n",
" <td>...</td>\n",
" <td>21.2</td>\n",
" <td>24.4</td>\n",
" <td>16.5</td>\n",
" <td>5.0</td>\n",
" <td>37.2</td>\n",
" <td>93.6</td>\n",
" <td>26.6</td>\n",
" <td>64.4</td>\n",
" <td>78.8</td>\n",
" <td>75.7</td>\n",
" <td>61.8</td>\n",
" <td>28.0</td>\n",
" <td>77.7</td>\n",
" <td>38.3</td>\n",
" <td>87.2</td>\n",
" <td>95.9</td>\n",
" <td>18.9</td>\n",
" <td>8.3</td>\n",
" <td>10.9</td>\n",
" <td>84.3</td>\n",
" <td>22.4</td>\n",
" <td>48.1</td>\n",
" <td>38.6</td>\n",
" <td>41.2</td>\n",
" <td>30.7</td>\n",
" <td>21.3</td>\n",
" <td>50.0</td>\n",
" <td>47.0</td>\n",
" <td>50.0</td>\n",
" <td>32.1</td>\n",
" <td>64.6</td>\n",
" <td>55.6</td>\n",
" <td>49.2</td>\n",
" <td>74.1</td>\n",
" <td>59.7</td>\n",
" <td>51.4</td>\n",
" <td>12.4</td>\n",
" <td>29.8</td>\n",
" <td>79.1</td>\n",
" <td>15.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NCIS/5</td>\n",
" <td>68.6</td>\n",
" <td>32.4</td>\n",
" <td>42.1</td>\n",
" <td>39.8</td>\n",
" <td>71.5</td>\n",
" <td>67.0</td>\n",
" <td>16.8</td>\n",
" <td>44.8</td>\n",
" <td>21.2</td>\n",
" <td>37.2</td>\n",
" <td>32.8</td>\n",
" <td>83.9</td>\n",
" <td>54.6</td>\n",
" <td>80.8</td>\n",
" <td>18.5</td>\n",
" <td>60.2</td>\n",
" <td>74.7</td>\n",
" <td>29.7</td>\n",
" <td>63.2</td>\n",
" <td>70.4</td>\n",
" <td>46.8</td>\n",
" <td>73.4</td>\n",
" <td>19.7</td>\n",
" <td>75.1</td>\n",
" <td>22.4</td>\n",
" <td>34.2</td>\n",
" <td>81.6</td>\n",
" <td>9.5</td>\n",
" <td>68.6</td>\n",
" <td>30.8</td>\n",
" <td>85.9</td>\n",
" <td>16.8</td>\n",
" <td>71.0</td>\n",
" <td>56.9</td>\n",
" <td>67.3</td>\n",
" <td>73.0</td>\n",
" <td>75.1</td>\n",
" <td>80.5</td>\n",
" <td>11.6</td>\n",
" <td>...</td>\n",
" <td>31.6</td>\n",
" <td>41.8</td>\n",
" <td>32.2</td>\n",
" <td>13.6</td>\n",
" <td>50.9</td>\n",
" <td>80.8</td>\n",
" <td>36.9</td>\n",
" <td>46.3</td>\n",
" <td>73.4</td>\n",
" <td>61.9</td>\n",
" <td>64.1</td>\n",
" <td>43.7</td>\n",
" <td>69.2</td>\n",
" <td>24.8</td>\n",
" <td>71.8</td>\n",
" <td>82.3</td>\n",
" <td>32.0</td>\n",
" <td>41.4</td>\n",
" <td>23.1</td>\n",
" <td>82.5</td>\n",
" <td>36.2</td>\n",
" <td>58.6</td>\n",
" <td>45.0</td>\n",
" <td>40.6</td>\n",
" <td>46.4</td>\n",
" <td>18.8</td>\n",
" <td>62.0</td>\n",
" <td>53.0</td>\n",
" <td>61.1</td>\n",
" <td>43.6</td>\n",
" <td>73.8</td>\n",
" <td>39.4</td>\n",
" <td>36.7</td>\n",
" <td>71.5</td>\n",
" <td>61.4</td>\n",
" <td>45.3</td>\n",
" <td>67.5</td>\n",
" <td>33.7</td>\n",
" <td>76.2</td>\n",
" <td>24.8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 269 columns</p>\n",
"</div>"
],
"text/plain": [
" Emotion BAP1 BAP2 BAP3 BAP4 ... BAP264 BAP265 BAP266 BAP267 BAP268\n",
"0 NCIS/1 86.5 82.1 74.8 7.9 ... 29.4 76.7 32.8 81.9 11.5\n",
"1 NCIS/2 18.5 88.2 24.3 14.4 ... 64.7 69.2 34.3 70.4 34.3\n",
"2 NCIS/3 16.0 86.3 11.9 64.8 ... 31.5 22.3 15.9 78.8 13.2\n",
"3 NCIS/4 63.5 64.7 29.1 34.1 ... 51.4 12.4 29.8 79.1 15.6\n",
"4 NCIS/5 68.6 32.4 42.1 39.8 ... 45.3 67.5 33.7 76.2 24.8\n",
"\n",
"[5 rows x 269 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "IHEFRNbWzcyc"
},
"source": [
"#first we seperate label column Emotion\n",
"label=data.Emotion.values\n",
"data=data.drop(['Emotion'],axis=1)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "L9oJxpp6zcyf",
"outputId": "c559ab0e-57cb-4191-ece4-c488e4a0d446",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 244
}
},
"source": [
"data.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>BAP1</th>\n",
" <th>BAP2</th>\n",
" <th>BAP3</th>\n",
" <th>BAP4</th>\n",
" <th>BAP5</th>\n",
" <th>BAP6</th>\n",
" <th>BAP7</th>\n",
" <th>BAP8</th>\n",
" <th>BAP9</th>\n",
" <th>BAP10</th>\n",
" <th>BAP11</th>\n",
" <th>BAP12</th>\n",
" <th>BAP13</th>\n",
" <th>BAP14</th>\n",
" <th>BAP15</th>\n",
" <th>BAP16</th>\n",
" <th>BAP17</th>\n",
" <th>BAP18</th>\n",
" <th>BAP19</th>\n",
" <th>BAP20</th>\n",
" <th>BAP21</th>\n",
" <th>BAP22</th>\n",
" <th>BAP23</th>\n",
" <th>BAP24</th>\n",
" <th>BAP25</th>\n",
" <th>BAP26</th>\n",
" <th>BAP27</th>\n",
" <th>BAP28</th>\n",
" <th>BAP29</th>\n",
" <th>BAP30</th>\n",
" <th>BAP31</th>\n",
" <th>BAP32</th>\n",
" <th>BAP33</th>\n",
" <th>BAP34</th>\n",
" <th>BAP35</th>\n",
" <th>BAP36</th>\n",
" <th>BAP37</th>\n",
" <th>BAP38</th>\n",
" <th>BAP39</th>\n",
" <th>BAP40</th>\n",
" <th>...</th>\n",
" <th>BAP229</th>\n",
" <th>BAP230</th>\n",
" <th>BAP231</th>\n",
" <th>BAP232</th>\n",
" <th>BAP233</th>\n",
" <th>BAP234</th>\n",
" <th>BAP235</th>\n",
" <th>BAP236</th>\n",
" <th>BAP237</th>\n",
" <th>BAP238</th>\n",
" <th>BAP239</th>\n",
" <th>BAP240</th>\n",
" <th>BAP241</th>\n",
" <th>BAP242</th>\n",
" <th>BAP243</th>\n",
" <th>BAP244</th>\n",
" <th>BAP245</th>\n",
" <th>BAP246</th>\n",
" <th>BAP247</th>\n",
" <th>BAP248</th>\n",
" <th>BAP249</th>\n",
" <th>BAP250</th>\n",
" <th>BAP251</th>\n",
" <th>BAP252</th>\n",
" <th>BAP253</th>\n",
" <th>BAP254</th>\n",
" <th>BAP255</th>\n",
" <th>BAP256</th>\n",
" <th>BAP257</th>\n",
" <th>BAP258</th>\n",
" <th>BAP259</th>\n",
" <th>BAP260</th>\n",
" <th>BAP261</th>\n",
" <th>BAP262</th>\n",
" <th>BAP263</th>\n",
" <th>BAP264</th>\n",
" <th>BAP265</th>\n",
" <th>BAP266</th>\n",
" <th>BAP267</th>\n",
" <th>BAP268</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>86.5</td>\n",
" <td>82.1</td>\n",
" <td>74.8</td>\n",
" <td>7.9</td>\n",
" <td>31.1</td>\n",
" <td>78.1</td>\n",
" <td>37.9</td>\n",
" <td>20.6</td>\n",
" <td>55.7</td>\n",
" <td>67.3</td>\n",
" <td>83.7</td>\n",
" <td>78.6</td>\n",
" <td>15.1</td>\n",
" <td>83.0</td>\n",
" <td>17.7</td>\n",
" <td>43.1</td>\n",
" <td>48.0</td>\n",
" <td>20.1</td>\n",
" <td>23.4</td>\n",
" <td>85.5</td>\n",
" <td>49.6</td>\n",
" <td>27.2</td>\n",
" <td>13.9</td>\n",
" <td>8.5</td>\n",
" <td>52.6</td>\n",
" <td>10.9</td>\n",
" <td>60.0</td>\n",
" <td>7.2</td>\n",
" <td>72.6</td>\n",
" <td>44.6</td>\n",
" <td>63.2</td>\n",
" <td>5.6</td>\n",
" <td>65.4</td>\n",
" <td>91.6</td>\n",
" <td>84.0</td>\n",
" <td>58.0</td>\n",
" <td>83.5</td>\n",
" <td>63.0</td>\n",
" <td>12.5</td>\n",
" <td>26.3</td>\n",
" <td>...</td>\n",
" <td>24.8</td>\n",
" <td>14.1</td>\n",
" <td>21.7</td>\n",
" <td>25.5</td>\n",
" <td>15.1</td>\n",
" <td>88.3</td>\n",
" <td>54.7</td>\n",
" <td>37.9</td>\n",
" <td>44.8</td>\n",
" <td>51.5</td>\n",
" <td>81.1</td>\n",
" <td>30.3</td>\n",
" <td>44.8</td>\n",
" <td>35.4</td>\n",
" <td>80.8</td>\n",
" <td>65.7</td>\n",
" <td>65.3</td>\n",
" <td>65.8</td>\n",
" <td>37.6</td>\n",
" <td>57.1</td>\n",
" <td>37.1</td>\n",
" <td>19.6</td>\n",
" <td>32.6</td>\n",
" <td>24.3</td>\n",
" <td>47.8</td>\n",
" <td>41.5</td>\n",
" <td>51.5</td>\n",
" <td>61.6</td>\n",
" <td>61.8</td>\n",
" <td>38.5</td>\n",
" <td>38.4</td>\n",
" <td>57.9</td>\n",
" <td>21.1</td>\n",
" <td>64.2</td>\n",
" <td>23.4</td>\n",
" <td>29.4</td>\n",
" <td>76.7</td>\n",
" <td>32.8</td>\n",
" <td>81.9</td>\n",
" <td>11.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>18.5</td>\n",
" <td>88.2</td>\n",
" <td>24.3</td>\n",
" <td>14.4</td>\n",
" <td>13.1</td>\n",
" <td>40.4</td>\n",
" <td>69.7</td>\n",
" <td>73.3</td>\n",
" <td>48.7</td>\n",
" <td>55.0</td>\n",
" <td>79.0</td>\n",
" <td>46.7</td>\n",
" <td>70.9</td>\n",
" <td>51.0</td>\n",
" <td>68.6</td>\n",
" <td>47.5</td>\n",
" <td>24.5</td>\n",
" <td>64.6</td>\n",
" <td>19.6</td>\n",
" <td>74.0</td>\n",
" <td>58.3</td>\n",
" <td>31.2</td>\n",
" <td>76.5</td>\n",
" <td>27.9</td>\n",
" <td>47.5</td>\n",
" <td>62.5</td>\n",
" <td>26.7</td>\n",
" <td>12.8</td>\n",
" <td>43.3</td>\n",
" <td>25.4</td>\n",
" <td>52.9</td>\n",
" <td>54.0</td>\n",
" <td>20.2</td>\n",
" <td>15.0</td>\n",
" <td>44.4</td>\n",
" <td>39.0</td>\n",
" <td>49.5</td>\n",
" <td>28.3</td>\n",
" <td>17.0</td>\n",
" <td>20.1</td>\n",
" <td>...</td>\n",
" <td>67.8</td>\n",
" <td>38.6</td>\n",
" <td>53.7</td>\n",
" <td>58.1</td>\n",
" <td>62.9</td>\n",
" <td>75.6</td>\n",
" <td>35.5</td>\n",
" <td>72.2</td>\n",
" <td>14.0</td>\n",
" <td>81.4</td>\n",
" <td>38.2</td>\n",
" <td>72.8</td>\n",
" <td>55.1</td>\n",
" <td>71.4</td>\n",
" <td>74.8</td>\n",
" <td>25.6</td>\n",
" <td>23.9</td>\n",
" <td>57.7</td>\n",
" <td>78.0</td>\n",
" <td>36.9</td>\n",
" <td>41.0</td>\n",
" <td>43.1</td>\n",
" <td>56.4</td>\n",
" <td>50.6</td>\n",
" <td>67.0</td>\n",
" <td>57.8</td>\n",
" <td>50.8</td>\n",
" <td>19.9</td>\n",
" <td>60.1</td>\n",
" <td>38.1</td>\n",
" <td>54.8</td>\n",
" <td>83.3</td>\n",
" <td>84.5</td>\n",
" <td>32.1</td>\n",
" <td>42.1</td>\n",
" <td>64.7</td>\n",
" <td>69.2</td>\n",
" <td>34.3</td>\n",
" <td>70.4</td>\n",
" <td>34.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>16.0</td>\n",
" <td>86.3</td>\n",
" <td>11.9</td>\n",
" <td>64.8</td>\n",
" <td>35.9</td>\n",
" <td>61.5</td>\n",
" <td>15.5</td>\n",
" <td>70.2</td>\n",
" <td>60.2</td>\n",
" <td>22.8</td>\n",
" <td>55.1</td>\n",
" <td>68.6</td>\n",
" <td>90.4</td>\n",
" <td>62.2</td>\n",
" <td>67.0</td>\n",
" <td>86.1</td>\n",
" <td>66.1</td>\n",
" <td>55.6</td>\n",
" <td>30.5</td>\n",
" <td>36.8</td>\n",
" <td>67.2</td>\n",
" <td>61.3</td>\n",
" <td>78.1</td>\n",
" <td>29.4</td>\n",
" <td>27.9</td>\n",
" <td>22.4</td>\n",
" <td>35.1</td>\n",
" <td>6.8</td>\n",
" <td>8.7</td>\n",
" <td>11.6</td>\n",
" <td>79.0</td>\n",
" <td>9.6</td>\n",
" <td>45.1</td>\n",
" <td>9.0</td>\n",
" <td>52.9</td>\n",
" <td>45.7</td>\n",
" <td>91.2</td>\n",
" <td>74.3</td>\n",
" <td>15.4</td>\n",
" <td>17.6</td>\n",
" <td>...</td>\n",
" <td>78.9</td>\n",
" <td>28.5</td>\n",
" <td>17.8</td>\n",
" <td>19.0</td>\n",
" <td>67.5</td>\n",
" <td>92.0</td>\n",
" <td>30.4</td>\n",
" <td>81.4</td>\n",
" <td>33.7</td>\n",
" <td>85.1</td>\n",
" <td>72.7</td>\n",
" <td>18.4</td>\n",
" <td>78.6</td>\n",
" <td>71.8</td>\n",
" <td>93.9</td>\n",
" <td>91.0</td>\n",
" <td>15.9</td>\n",
" <td>24.2</td>\n",
" <td>37.1</td>\n",
" <td>54.1</td>\n",
" <td>35.6</td>\n",
" <td>42.9</td>\n",
" <td>42.4</td>\n",
" <td>91.0</td>\n",
" <td>63.8</td>\n",
" <td>40.8</td>\n",
" <td>14.9</td>\n",
" <td>22.6</td>\n",
" <td>27.8</td>\n",
" <td>52.7</td>\n",
" <td>18.9</td>\n",
" <td>75.2</td>\n",
" <td>93.7</td>\n",
" <td>49.6</td>\n",
" <td>36.0</td>\n",
" <td>31.5</td>\n",
" <td>22.3</td>\n",
" <td>15.9</td>\n",
" <td>78.8</td>\n",
" <td>13.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>63.5</td>\n",
" <td>64.7</td>\n",
" <td>29.1</td>\n",
" <td>34.1</td>\n",
" <td>24.5</td>\n",
" <td>84.2</td>\n",
" <td>7.1</td>\n",
" <td>49.3</td>\n",
" <td>14.1</td>\n",
" <td>33.1</td>\n",
" <td>70.3</td>\n",
" <td>88.1</td>\n",
" <td>46.5</td>\n",
" <td>79.4</td>\n",
" <td>15.6</td>\n",
" <td>55.4</td>\n",
" <td>82.9</td>\n",
" <td>63.7</td>\n",
" <td>59.7</td>\n",
" <td>63.1</td>\n",
" <td>72.5</td>\n",
" <td>74.3</td>\n",
" <td>23.8</td>\n",
" <td>46.3</td>\n",
" <td>17.9</td>\n",
" <td>9.9</td>\n",
" <td>84.3</td>\n",
" <td>6.7</td>\n",
" <td>61.2</td>\n",
" <td>21.6</td>\n",
" <td>88.2</td>\n",
" <td>7.2</td>\n",
" <td>78.8</td>\n",
" <td>32.0</td>\n",
" <td>84.0</td>\n",
" <td>78.8</td>\n",
" <td>90.4</td>\n",
" <td>77.5</td>\n",
" <td>15.0</td>\n",
" <td>37.6</td>\n",
" <td>...</td>\n",
" <td>21.2</td>\n",
" <td>24.4</td>\n",
" <td>16.5</td>\n",
" <td>5.0</td>\n",
" <td>37.2</td>\n",
" <td>93.6</td>\n",
" <td>26.6</td>\n",
" <td>64.4</td>\n",
" <td>78.8</td>\n",
" <td>75.7</td>\n",
" <td>61.8</td>\n",
" <td>28.0</td>\n",
" <td>77.7</td>\n",
" <td>38.3</td>\n",
" <td>87.2</td>\n",
" <td>95.9</td>\n",
" <td>18.9</td>\n",
" <td>8.3</td>\n",
" <td>10.9</td>\n",
" <td>84.3</td>\n",
" <td>22.4</td>\n",
" <td>48.1</td>\n",
" <td>38.6</td>\n",
" <td>41.2</td>\n",
" <td>30.7</td>\n",
" <td>21.3</td>\n",
" <td>50.0</td>\n",
" <td>47.0</td>\n",
" <td>50.0</td>\n",
" <td>32.1</td>\n",
" <td>64.6</td>\n",
" <td>55.6</td>\n",
" <td>49.2</td>\n",
" <td>74.1</td>\n",
" <td>59.7</td>\n",
" <td>51.4</td>\n",
" <td>12.4</td>\n",
" <td>29.8</td>\n",
" <td>79.1</td>\n",
" <td>15.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>68.6</td>\n",
" <td>32.4</td>\n",
" <td>42.1</td>\n",
" <td>39.8</td>\n",
" <td>71.5</td>\n",
" <td>67.0</td>\n",
" <td>16.8</td>\n",
" <td>44.8</td>\n",
" <td>21.2</td>\n",
" <td>37.2</td>\n",
" <td>32.8</td>\n",
" <td>83.9</td>\n",
" <td>54.6</td>\n",
" <td>80.8</td>\n",
" <td>18.5</td>\n",
" <td>60.2</td>\n",
" <td>74.7</td>\n",
" <td>29.7</td>\n",
" <td>63.2</td>\n",
" <td>70.4</td>\n",
" <td>46.8</td>\n",
" <td>73.4</td>\n",
" <td>19.7</td>\n",
" <td>75.1</td>\n",
" <td>22.4</td>\n",
" <td>34.2</td>\n",
" <td>81.6</td>\n",
" <td>9.5</td>\n",
" <td>68.6</td>\n",
" <td>30.8</td>\n",
" <td>85.9</td>\n",
" <td>16.8</td>\n",
" <td>71.0</td>\n",
" <td>56.9</td>\n",
" <td>67.3</td>\n",
" <td>73.0</td>\n",
" <td>75.1</td>\n",
" <td>80.5</td>\n",
" <td>11.6</td>\n",
" <td>40.8</td>\n",
" <td>...</td>\n",
" <td>31.6</td>\n",
" <td>41.8</td>\n",
" <td>32.2</td>\n",
" <td>13.6</td>\n",
" <td>50.9</td>\n",
" <td>80.8</td>\n",
" <td>36.9</td>\n",
" <td>46.3</td>\n",
" <td>73.4</td>\n",
" <td>61.9</td>\n",
" <td>64.1</td>\n",
" <td>43.7</td>\n",
" <td>69.2</td>\n",
" <td>24.8</td>\n",
" <td>71.8</td>\n",
" <td>82.3</td>\n",
" <td>32.0</td>\n",
" <td>41.4</td>\n",
" <td>23.1</td>\n",
" <td>82.5</td>\n",
" <td>36.2</td>\n",
" <td>58.6</td>\n",
" <td>45.0</td>\n",
" <td>40.6</td>\n",
" <td>46.4</td>\n",
" <td>18.8</td>\n",
" <td>62.0</td>\n",
" <td>53.0</td>\n",
" <td>61.1</td>\n",
" <td>43.6</td>\n",
" <td>73.8</td>\n",
" <td>39.4</td>\n",
" <td>36.7</td>\n",
" <td>71.5</td>\n",
" <td>61.4</td>\n",
" <td>45.3</td>\n",
" <td>67.5</td>\n",
" <td>33.7</td>\n",
" <td>76.2</td>\n",
" <td>24.8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 268 columns</p>\n",
"</div>"
],
"text/plain": [
" BAP1 BAP2 BAP3 BAP4 BAP5 ... BAP264 BAP265 BAP266 BAP267 BAP268\n",
"0 86.5 82.1 74.8 7.9 31.1 ... 29.4 76.7 32.8 81.9 11.5\n",
"1 18.5 88.2 24.3 14.4 13.1 ... 64.7 69.2 34.3 70.4 34.3\n",
"2 16.0 86.3 11.9 64.8 35.9 ... 31.5 22.3 15.9 78.8 13.2\n",
"3 63.5 64.7 29.1 34.1 24.5 ... 51.4 12.4 29.8 79.1 15.6\n",
"4 68.6 32.4 42.1 39.8 71.5 ... 45.3 67.5 33.7 76.2 24.8\n",
"\n",
"[5 rows x 268 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Qz1Mig3Vzcyi"
},
"source": [
"#Lets enumerate labels \n",
"mapping=dict()\n",
"for x,y in enumerate(label):\n",
" mapping[x]=y\n",
"labels_enum=list(mapping.keys())"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "-zjQhyzAzcyl",
"outputId": "b7f0c07f-482c-41b1-b506-c91998e38169",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"labels_enum[:10]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "F-cG50cAzcyn",
"outputId": "a96e8404-c3c5-4b19-9bc8-9182b416686c",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"#Now we will filter only first 20 columns from dataset for data\n",
"value=data[['BAP1', 'BAP2', 'BAP3', 'BAP4', 'BAP5', 'BAP6', 'BAP7', 'BAP8', 'BAP9', 'BAP10', 'BAP11', 'BAP12', 'BAP13', 'BAP14', 'BAP15', 'BAP16', 'BAP17', 'BAP18', 'BAP19', 'BAP20']].values\n",
"value[:5]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[86.5, 82.1, 74.8, 7.9, 31.1, 78.1, 37.9, 20.6, 55.7, 67.3, 83.7,\n",
" 78.6, 15.1, 83. , 17.7, 43.1, 48. , 20.1, 23.4, 85.5],\n",
" [18.5, 88.2, 24.3, 14.4, 13.1, 40.4, 69.7, 73.3, 48.7, 55. , 79. ,\n",
" 46.7, 70.9, 51. , 68.6, 47.5, 24.5, 64.6, 19.6, 74. ],\n",
" [16. , 86.3, 11.9, 64.8, 35.9, 61.5, 15.5, 70.2, 60.2, 22.8, 55.1,\n",
" 68.6, 90.4, 62.2, 67. , 86.1, 66.1, 55.6, 30.5, 36.8],\n",
" [63.5, 64.7, 29.1, 34.1, 24.5, 84.2, 7.1, 49.3, 14.1, 33.1, 70.3,\n",
" 88.1, 46.5, 79.4, 15.6, 55.4, 82.9, 63.7, 59.7, 63.1],\n",
" [68.6, 32.4, 42.1, 39.8, 71.5, 67. , 16.8, 44.8, 21.2, 37.2, 32.8,\n",
" 83.9, 54.6, 80.8, 18.5, 60.2, 74.7, 29.7, 63.2, 70.4]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "TUDJZ9h90J2n"
},
"source": [
"def process(arr):\n",
" narr=[]\n",
" for each in arr:\n",
" narr.append(each/100)\n",
" return narr "
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "tqWeooAJ2xWy"
},
"source": [
"nvalue=process(value)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "B57C8Q4k0ZaE",
"outputId": "657e14db-9d64-45e1-9c20-d18962109d42",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"nvalue[:4]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[array([0.865, 0.821, 0.748, 0.079, 0.311, 0.781, 0.379, 0.206, 0.557,\n",
" 0.673, 0.837, 0.786, 0.151, 0.83 , 0.177, 0.431, 0.48 , 0.201,\n",
" 0.234, 0.855]),\n",
" array([0.185, 0.882, 0.243, 0.144, 0.131, 0.404, 0.697, 0.733, 0.487,\n",
" 0.55 , 0.79 , 0.467, 0.709, 0.51 , 0.686, 0.475, 0.245, 0.646,\n",
" 0.196, 0.74 ]),\n",
" array([0.16 , 0.863, 0.119, 0.648, 0.359, 0.615, 0.155, 0.702, 0.602,\n",
" 0.228, 0.551, 0.686, 0.904, 0.622, 0.67 , 0.861, 0.661, 0.556,\n",
" 0.305, 0.368]),\n",
" array([0.635, 0.647, 0.291, 0.341, 0.245, 0.842, 0.071, 0.493, 0.141,\n",
" 0.331, 0.703, 0.881, 0.465, 0.794, 0.156, 0.554, 0.829, 0.637,\n",
" 0.597, 0.631])]"
]
},
"metadata": {
"tags": []
},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "8b5bWqOPriTn",
"outputId": "42f54bc2-d1c7-49ea-fabf-86f688a6071e",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"value.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(800, 20)"
]
},
"metadata": {
"tags": []
},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "oFA9737xzcyq"
},
"source": [
"from sklearn.linear_model import LogisticRegression"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "pdh3fiNuzcyt",
"outputId": "f0b9530f-b93f-46e3-cccd-b32b49999509",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"model=LogisticRegression()\n",
"model.fit(value,labels_enum)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
"\n",
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
"Please also refer to the documentation for alternative solver options:\n",
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
" extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
],
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
" multi_class='auto', n_jobs=None, penalty='l2',\n",
" random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
" warm_start=False)"
]
},
"metadata": {
"tags": []
},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "z22d1vzC2DJs",
"outputId": "8f6aab51-9ff6-45d4-dd6a-0714f8c0e3e1",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"import numpy as np\n",
"data_test=np.array([[45.2,55.5,20.3,10.3,34.2,56.1,30.2,60,26.3,54,11,89,78,22,32,68,75,25,50,50]])\n",
"val_test=process(data_test)\n",
"prediction=model.predict(val_test)\n",
"prediction"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([272])"
]
},
"metadata": {
"tags": []
},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "x5yJkyqV3ce5",
"outputId": "78dbe65f-58c4-4756-ad83-f10e81470c70",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"from sklearn.externals import joblib\n",
"joblib_file = \"joblib_model.pkl\"\n",
"joblib.dump(model, joblib_file)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
" warnings.warn(msg, category=FutureWarning)\n"
],
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['joblib_model.pkl']"
]
},
"metadata": {
"tags": []
},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "JBkIpodK3o6I"
},
"source": [
"joblib_model = joblib.load(joblib_file)"
],
"execution_count": null,
"outputs": []
}
]
}
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