└── Student.ipynb


/Student.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 19,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import pandas as pd\n",
 10 |     "from sklearn.model_selection import train_test_split\n",
 11 |     "from sklearn.naive_bayes import GaussianNB\n",
 12 |     "from sklearn.metrics import accuracy_score, classification_report\n",
 13 |     "from sklearn.preprocessing import LabelEncoder\n",
 14 |     "from sklearn.metrics import confusion_matrix\n",
 15 |     "import matplotlib.pyplot as plt\n",
 16 |     "import seaborn as sns"
 17 |    ]
 18 |   },
 19 |   {
 20 |    "cell_type": "code",
 21 |    "execution_count": 20,
 22 |    "metadata": {},
 23 |    "outputs": [
 24 |     {
 25 |      "data": {
 26 |       "text/html": [
 27 |        "<div>\n",
 28 |        "<style scoped>\n",
 29 |        "    .dataframe tbody tr th:only-of-type {\n",
 30 |        "        vertical-align: middle;\n",
 31 |        "    }\n",
 32 |        "\n",
 33 |        "    .dataframe tbody tr th {\n",
 34 |        "        vertical-align: top;\n",
 35 |        "    }\n",
 36 |        "\n",
 37 |        "    .dataframe thead th {\n",
 38 |        "        text-align: right;\n",
 39 |        "    }\n",
 40 |        "</style>\n",
 41 |        "<table border=\"1\" class=\"dataframe\">\n",
 42 |        "  <thead>\n",
 43 |        "    <tr style=\"text-align: right;\">\n",
 44 |        "      <th></th>\n",
 45 |        "      <th>gender</th>\n",
 46 |        "      <th>race/ethnicity</th>\n",
 47 |        "      <th>lunch</th>\n",
 48 |        "      <th>math score</th>\n",
 49 |        "      <th>reading score</th>\n",
 50 |        "      <th>writing score</th>\n",
 51 |        "    </tr>\n",
 52 |        "  </thead>\n",
 53 |        "  <tbody>\n",
 54 |        "    <tr>\n",
 55 |        "      <th>0</th>\n",
 56 |        "      <td>female</td>\n",
 57 |        "      <td>group B</td>\n",
 58 |        "      <td>standard</td>\n",
 59 |        "      <td>72</td>\n",
 60 |        "      <td>72</td>\n",
 61 |        "      <td>74</td>\n",
 62 |        "    </tr>\n",
 63 |        "    <tr>\n",
 64 |        "      <th>1</th>\n",
 65 |        "      <td>female</td>\n",
 66 |        "      <td>group C</td>\n",
 67 |        "      <td>standard</td>\n",
 68 |        "      <td>69</td>\n",
 69 |        "      <td>90</td>\n",
 70 |        "      <td>88</td>\n",
 71 |        "    </tr>\n",
 72 |        "    <tr>\n",
 73 |        "      <th>2</th>\n",
 74 |        "      <td>female</td>\n",
 75 |        "      <td>group B</td>\n",
 76 |        "      <td>standard</td>\n",
 77 |        "      <td>90</td>\n",
 78 |        "      <td>95</td>\n",
 79 |        "      <td>93</td>\n",
 80 |        "    </tr>\n",
 81 |        "    <tr>\n",
 82 |        "      <th>3</th>\n",
 83 |        "      <td>male</td>\n",
 84 |        "      <td>group A</td>\n",
 85 |        "      <td>free/reduced</td>\n",
 86 |        "      <td>47</td>\n",
 87 |        "      <td>57</td>\n",
 88 |        "      <td>44</td>\n",
 89 |        "    </tr>\n",
 90 |        "    <tr>\n",
 91 |        "      <th>4</th>\n",
 92 |        "      <td>male</td>\n",
 93 |        "      <td>group C</td>\n",
 94 |        "      <td>standard</td>\n",
 95 |        "      <td>76</td>\n",
 96 |        "      <td>78</td>\n",
 97 |        "      <td>75</td>\n",
 98 |        "    </tr>\n",
 99 |        "    <tr>\n",
100 |        "      <th>...</th>\n",
101 |        "      <td>...</td>\n",
102 |        "      <td>...</td>\n",
103 |        "      <td>...</td>\n",
104 |        "      <td>...</td>\n",
105 |        "      <td>...</td>\n",
106 |        "      <td>...</td>\n",
107 |        "    </tr>\n",
108 |        "    <tr>\n",
109 |        "      <th>995</th>\n",
110 |        "      <td>female</td>\n",
111 |        "      <td>group E</td>\n",
112 |        "      <td>standard</td>\n",
113 |        "      <td>88</td>\n",
114 |        "      <td>99</td>\n",
115 |        "      <td>95</td>\n",
116 |        "    </tr>\n",
117 |        "    <tr>\n",
118 |        "      <th>996</th>\n",
119 |        "      <td>male</td>\n",
120 |        "      <td>group C</td>\n",
121 |        "      <td>free/reduced</td>\n",
122 |        "      <td>62</td>\n",
123 |        "      <td>55</td>\n",
124 |        "      <td>55</td>\n",
125 |        "    </tr>\n",
126 |        "    <tr>\n",
127 |        "      <th>997</th>\n",
128 |        "      <td>female</td>\n",
129 |        "      <td>group C</td>\n",
130 |        "      <td>free/reduced</td>\n",
131 |        "      <td>59</td>\n",
132 |        "      <td>71</td>\n",
133 |        "      <td>65</td>\n",
134 |        "    </tr>\n",
135 |        "    <tr>\n",
136 |        "      <th>998</th>\n",
137 |        "      <td>female</td>\n",
138 |        "      <td>group D</td>\n",
139 |        "      <td>standard</td>\n",
140 |        "      <td>68</td>\n",
141 |        "      <td>78</td>\n",
142 |        "      <td>77</td>\n",
143 |        "    </tr>\n",
144 |        "    <tr>\n",
145 |        "      <th>999</th>\n",
146 |        "      <td>female</td>\n",
147 |        "      <td>group D</td>\n",
148 |        "      <td>free/reduced</td>\n",
149 |        "      <td>77</td>\n",
150 |        "      <td>86</td>\n",
151 |        "      <td>86</td>\n",
152 |        "    </tr>\n",
153 |        "  </tbody>\n",
154 |        "</table>\n",
155 |        "<p>1000 rows × 6 columns</p>\n",
156 |        "</div>"
157 |       ],
158 |       "text/plain": [
159 |        "     gender race/ethnicity         lunch  math score  reading score  \\\n",
160 |        "0    female        group B      standard          72             72   \n",
161 |        "1    female        group C      standard          69             90   \n",
162 |        "2    female        group B      standard          90             95   \n",
163 |        "3      male        group A  free/reduced          47             57   \n",
164 |        "4      male        group C      standard          76             78   \n",
165 |        "..      ...            ...           ...         ...            ...   \n",
166 |        "995  female        group E      standard          88             99   \n",
167 |        "996    male        group C  free/reduced          62             55   \n",
168 |        "997  female        group C  free/reduced          59             71   \n",
169 |        "998  female        group D      standard          68             78   \n",
170 |        "999  female        group D  free/reduced          77             86   \n",
171 |        "\n",
172 |        "     writing score  \n",
173 |        "0               74  \n",
174 |        "1               88  \n",
175 |        "2               93  \n",
176 |        "3               44  \n",
177 |        "4               75  \n",
178 |        "..             ...  \n",
179 |        "995             95  \n",
180 |        "996             55  \n",
181 |        "997             65  \n",
182 |        "998             77  \n",
183 |        "999             86  \n",
184 |        "\n",
185 |        "[1000 rows x 6 columns]"
186 |       ]
187 |      },
188 |      "execution_count": 20,
189 |      "metadata": {},
190 |      "output_type": "execute_result"
191 |     }
192 |    ],
193 |    "source": [
194 |     "data=pd.read_csv(\"StudentsPerformance.csv\")\n",
195 |     "data"
196 |    ]
197 |   },
198 |   {
199 |    "cell_type": "code",
200 |    "execution_count": 21,
201 |    "metadata": {},
202 |    "outputs": [
203 |     {
204 |      "name": "stdout",
205 |      "output_type": "stream",
206 |      "text": [
207 |       "     reading score  writing score\n",
208 |       "0               72             74\n",
209 |       "1               90             88\n",
210 |       "2               95             93\n",
211 |       "3               57             44\n",
212 |       "4               78             75\n",
213 |       "..             ...            ...\n",
214 |       "995             99             95\n",
215 |       "996             55             55\n",
216 |       "997             71             65\n",
217 |       "998             78             77\n",
218 |       "999             86             86\n",
219 |       "\n",
220 |       "[1000 rows x 2 columns]\n",
221 |       "0      72\n",
222 |       "1      69\n",
223 |       "2      90\n",
224 |       "3      47\n",
225 |       "4      76\n",
226 |       "       ..\n",
227 |       "995    88\n",
228 |       "996    62\n",
229 |       "997    59\n",
230 |       "998    68\n",
231 |       "999    77\n",
232 |       "Name: math score, Length: 1000, dtype: int64\n"
233 |      ]
234 |     }
235 |    ],
236 |    "source": [
237 |     "x=data.iloc[:,4:6]\n",
238 |     "y=data['math score']\n",
239 |     "print(x)\n",
240 |     "print(y)"
241 |    ]
242 |   },
243 |   {
244 |    "cell_type": "code",
245 |    "execution_count": 22,
246 |    "metadata": {},
247 |    "outputs": [],
248 |    "source": [
249 |     "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
250 |    ]
251 |   },
252 |   {
253 |    "cell_type": "code",
254 |    "execution_count": 23,
255 |    "metadata": {},
256 |    "outputs": [
257 |     {
258 |      "data": {
259 |       "text/html": [
260 |        "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
261 |       ],
262 |       "text/plain": [
263 |        "GaussianNB()"
264 |       ]
265 |      },
266 |      "execution_count": 23,
267 |      "metadata": {},
268 |      "output_type": "execute_result"
269 |     }
270 |    ],
271 |    "source": [
272 |     "model=GaussianNB()\n",
273 |     "model.fit(x_train, y_train)"
274 |    ]
275 |   },
276 |   {
277 |    "cell_type": "code",
278 |    "execution_count": 24,
279 |    "metadata": {},
280 |    "outputs": [
281 |     {
282 |      "data": {
283 |       "text/plain": [
284 |        "array([ 81,  65,  65,  65,  81,  74,  62,  62,  65,  53,  49,  35,  81,\n",
285 |        "        62,  81,  81,  37,  41,  54,  62,  65,  49,  65,  49,  74,  65,\n",
286 |        "        65,  49,  41,  61,  61,  62,  61,  65,  65,  49,  65,  74,  65,\n",
287 |        "        35,  65,  62,  62,  62,  81,  62,  65,  32,  81,  81,  65,  65,\n",
288 |        "        74,  54,  65,  65,  81,  49,  76,  97,  41,  81,  74,  62,  87,\n",
289 |        "        74,  65,  49,  62,  97,  62,  87,  54,  54,  61,  74,  87,  54,\n",
290 |        "        62,  49,  61,  32,  65,  65,  65,  62,  74,  65,  87,  35,  65,\n",
291 |        "        96,  54,  65,  32,  62,  62,  62,  65,  65,  65,  49,  49,  65,\n",
292 |        "        65,  62,  41,  65,  81,  54,  46,  81,  49,  62,  81,  54,  49,\n",
293 |        "        46,  62,  74,  65,  62,  81,  74,  62,  62,  74,  65,  41,  65,\n",
294 |        "        81,  65,  41, 100,  65,  41,  97,  74,  65,  49,  49,  65,  61,\n",
295 |        "        48,  65,  54,  32,  65,  49,  81,  81,  81,  62,  61,  96,  65,\n",
296 |        "        49,  65,  65,  49,  65,  61,  81,  49,  81,  65,  49,  81,  65,\n",
297 |        "        74,  81,  65,  97,  81,  53,  32,  49,  81,  96,  74,  62,  65,\n",
298 |        "        96,  61,  81,  54,  65,  61,  62,  81,  49,  49,  62,  74,  50,\n",
299 |        "        54,  49,  74,  65,  65], dtype=int64)"
300 |       ]
301 |      },
302 |      "execution_count": 24,
303 |      "metadata": {},
304 |      "output_type": "execute_result"
305 |     }
306 |    ],
307 |    "source": [
308 |     "y_pred=model.predict(x_test)\n",
309 |     "y_pred"
310 |    ]
311 |   },
312 |   {
313 |    "cell_type": "code",
314 |    "execution_count": 25,
315 |    "metadata": {},
316 |    "outputs": [
317 |     {
318 |      "name": "stdout",
319 |      "output_type": "stream",
320 |      "text": [
321 |       "              precision    recall  f1-score   support\n",
322 |       "\n",
323 |       "           0       0.00      0.00      0.00         1\n",
324 |       "          19       0.00      0.00      0.00         1\n",
325 |       "          27       0.00      0.00      0.00         1\n",
326 |       "          28       0.00      0.00      0.00         1\n",
327 |       "          29       0.00      0.00      0.00         2\n",
328 |       "          30       0.00      0.00      0.00         1\n",
329 |       "          32       0.00      0.00      0.00         0\n",
330 |       "          33       0.00      0.00      0.00         1\n",
331 |       "          34       0.00      0.00      0.00         1\n",
332 |       "          35       0.00      0.00      0.00         0\n",
333 |       "          36       0.00      0.00      0.00         1\n",
334 |       "          37       0.00      0.00      0.00         1\n",
335 |       "          40       0.00      0.00      0.00         2\n",
336 |       "          41       0.00      0.00      0.00         1\n",
337 |       "          43       0.00      0.00      0.00         1\n",
338 |       "          44       0.00      0.00      0.00         1\n",
339 |       "          45       0.00      0.00      0.00         7\n",
340 |       "          46       0.00      0.00      0.00         2\n",
341 |       "          48       0.00      0.00      0.00         3\n",
342 |       "          49       0.13      0.50      0.21         6\n",
343 |       "          50       0.00      0.00      0.00         2\n",
344 |       "          51       0.00      0.00      0.00         5\n",
345 |       "          52       0.00      0.00      0.00         1\n",
346 |       "          53       0.00      0.00      0.00         3\n",
347 |       "          54       0.00      0.00      0.00         4\n",
348 |       "          55       0.00      0.00      0.00         4\n",
349 |       "          56       0.00      0.00      0.00         4\n",
350 |       "          57       0.00      0.00      0.00         2\n",
351 |       "          58       0.00      0.00      0.00         6\n",
352 |       "          59       0.00      0.00      0.00         5\n",
353 |       "          60       0.00      0.00      0.00         4\n",
354 |       "          61       0.00      0.00      0.00         6\n",
355 |       "          62       0.11      0.43      0.18         7\n",
356 |       "          63       0.00      0.00      0.00         3\n",
357 |       "          64       0.00      0.00      0.00         4\n",
358 |       "          65       0.02      0.20      0.04         5\n",
359 |       "          66       0.00      0.00      0.00         5\n",
360 |       "          67       0.00      0.00      0.00         6\n",
361 |       "          68       0.00      0.00      0.00         5\n",
362 |       "          69       0.00      0.00      0.00         7\n",
363 |       "          70       0.00      0.00      0.00         3\n",
364 |       "          71       0.00      0.00      0.00         6\n",
365 |       "          73       0.00      0.00      0.00         8\n",
366 |       "          74       0.06      0.25      0.10         4\n",
367 |       "          75       0.00      0.00      0.00         5\n",
368 |       "          76       0.00      0.00      0.00         4\n",
369 |       "          77       0.00      0.00      0.00         5\n",
370 |       "          78       0.00      0.00      0.00         3\n",
371 |       "          79       0.00      0.00      0.00         5\n",
372 |       "          80       0.00      0.00      0.00         4\n",
373 |       "          81       0.04      0.20      0.06         5\n",
374 |       "          82       0.00      0.00      0.00         3\n",
375 |       "          83       0.00      0.00      0.00         1\n",
376 |       "          84       0.00      0.00      0.00         4\n",
377 |       "          85       0.00      0.00      0.00         5\n",
378 |       "          87       0.00      0.00      0.00         4\n",
379 |       "          88       0.00      0.00      0.00         3\n",
380 |       "          89       0.00      0.00      0.00         1\n",
381 |       "          90       0.00      0.00      0.00         2\n",
382 |       "          91       0.00      0.00      0.00         1\n",
383 |       "          93       0.00      0.00      0.00         1\n",
384 |       "          96       0.00      0.00      0.00         0\n",
385 |       "          97       0.00      0.00      0.00         0\n",
386 |       "         100       0.00      0.00      0.00         1\n",
387 |       "\n",
388 |       "    accuracy                           0.04       200\n",
389 |       "   macro avg       0.01      0.02      0.01       200\n",
390 |       "weighted avg       0.01      0.04      0.02       200\n",
391 |       "\n"
392 |      ]
393 |     },
394 |     {
395 |      "name": "stderr",
396 |      "output_type": "stream",
397 |      "text": [
398 |       "c:\\Users\\muthu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
399 |       "  _warn_prf(average, modifier, msg_start, len(result))\n",
400 |       "c:\\Users\\muthu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
401 |       "  _warn_prf(average, modifier, msg_start, len(result))\n",
402 |       "c:\\Users\\muthu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
403 |       "  _warn_prf(average, modifier, msg_start, len(result))\n",
404 |       "c:\\Users\\muthu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
405 |       "  _warn_prf(average, modifier, msg_start, len(result))\n",
406 |       "c:\\Users\\muthu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
407 |       "  _warn_prf(average, modifier, msg_start, len(result))\n",
408 |       "c:\\Users\\muthu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1471: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
409 |       "  _warn_prf(average, modifier, msg_start, len(result))\n"
410 |      ]
411 |     }
412 |    ],
413 |    "source": [
414 |     "print(classification_report(y_test, y_pred))"
415 |    ]
416 |   },
417 |   {
418 |    "cell_type": "code",
419 |    "execution_count": 26,
420 |    "metadata": {},
421 |    "outputs": [
422 |     {
423 |      "data": {
424 |       "text/plain": [
425 |        "array([[0, 0, 0, ..., 0, 0, 0],\n",
426 |        "       [0, 0, 0, ..., 0, 0, 0],\n",
427 |        "       [0, 0, 0, ..., 0, 0, 0],\n",
428 |        "       ...,\n",
429 |        "       [0, 0, 0, ..., 0, 0, 0],\n",
430 |        "       [0, 0, 0, ..., 0, 0, 0],\n",
431 |        "       [0, 0, 0, ..., 1, 0, 0]], dtype=int64)"
432 |       ]
433 |      },
434 |      "execution_count": 26,
435 |      "metadata": {},
436 |      "output_type": "execute_result"
437 |     }
438 |    ],
439 |    "source": [
440 |     "conf_matrix=confusion_matrix(y_test,y_pred)\n",
441 |     "conf_matrix"
442 |    ]
443 |   },
444 |   {
445 |    "cell_type": "code",
446 |    "execution_count": 27,
447 |    "metadata": {},
448 |    "outputs": [
449 |     {
450 |      "data": {
451 |       "image/png": 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",
452 |       "text/plain": [
453 |        "<Figure size 640x480 with 1 Axes>"
454 |       ]
455 |      },
456 |      "metadata": {},
457 |      "output_type": "display_data"
458 |     }
459 |    ],
460 |    "source": [
461 |     "sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False)\n",
462 |     "plt.title(\"Confusion Matrix\")\n",
463 |     "plt.xlabel(\"Predicted Labels\")\n",
464 |     "plt.ylabel(\"True Labels\")\n",
465 |     "plt.show()"
466 |    ]
467 |   },
468 |   {
469 |    "cell_type": "code",
470 |    "execution_count": 28,
471 |    "metadata": {},
472 |    "outputs": [
473 |     {
474 |      "data": {
475 |       "image/png": 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",
476 |       "text/plain": [
477 |        "<Figure size 640x480 with 1 Axes>"
478 |       ]
479 |      },
480 |      "metadata": {},
481 |      "output_type": "display_data"
482 |     }
483 |    ],
484 |    "source": [
485 |     "plt.bar(y_test,y_pred)\n",
486 |     "plt.xlabel('reading score & writing score')\n",
487 |     "plt.ylabel('math score')\n",
488 |     "plt.title('Risks Factor')\n",
489 |     "plt.show()"
490 |    ]
491 |   }
492 |  ],
493 |  "metadata": {
494 |   "kernelspec": {
495 |    "display_name": "Python 3",
496 |    "language": "python",
497 |    "name": "python3"
498 |   },
499 |   "language_info": {
500 |    "codemirror_mode": {
501 |     "name": "ipython",
502 |     "version": 3
503 |    },
504 |    "file_extension": ".py",
505 |    "mimetype": "text/x-python",
506 |    "name": "python",
507 |    "nbconvert_exporter": "python",
508 |    "pygments_lexer": "ipython3",
509 |    "version": "3.12.0"
510 |   }
511 |  },
512 |  "nbformat": 4,
513 |  "nbformat_minor": 2
514 | }
515 | 


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