├── README.md
└── Task_1_Data_Analytics.ipynb


/README.md:
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1 | # Prediction-using-Supervised-ML
2 | Data Science and Business Analytics Task-1 (Predict the percentage of an student based on the no. of study hours)
3 | Using simple linear regression model, forecasting the marks of a student based on the numbers of hours studied per day.
4 | Tool(s) Used - Python (Jupyter Notebook)
5 | 


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/Task_1_Data_Analytics.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# **THE SPARKS FOUNDATION**\n",
  8 |     "\n",
  9 |     "## **TASK 1 - Prediction using Supervised ML**\n",
 10 |     "To Predict the percentage of marks of the students based on the number of hours they studied\n",
 11 |     "\n",
 12 |     "### *Author - Krishna Bansal*"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 51,
 18 |    "metadata": {},
 19 |    "outputs": [],
 20 |    "source": [
 21 |     "# importing the required libraries\n",
 22 |     "import pandas as pd\n",
 23 |     "import numpy as np\n",
 24 |     "import matplotlib.pyplot as plt \n",
 25 |     "import seaborn as sns\n",
 26 |     "from sklearn.model_selection import train_test_split\n",
 27 |     "from sklearn.linear_model import LinearRegression\n",
 28 |     "from sklearn.metrics import mean_absolute_error"
 29 |    ]
 30 |   },
 31 |   {
 32 |    "cell_type": "code",
 33 |    "execution_count": 52,
 34 |    "metadata": {},
 35 |    "outputs": [
 36 |     {
 37 |      "data": {
 38 |       "text/html": [
 39 |        "<div>\n",
 40 |        "<style scoped>\n",
 41 |        "    .dataframe tbody tr th:only-of-type {\n",
 42 |        "        vertical-align: middle;\n",
 43 |        "    }\n",
 44 |        "\n",
 45 |        "    .dataframe tbody tr th {\n",
 46 |        "        vertical-align: top;\n",
 47 |        "    }\n",
 48 |        "\n",
 49 |        "    .dataframe thead th {\n",
 50 |        "        text-align: right;\n",
 51 |        "    }\n",
 52 |        "</style>\n",
 53 |        "<table border=\"1\" class=\"dataframe\">\n",
 54 |        "  <thead>\n",
 55 |        "    <tr style=\"text-align: right;\">\n",
 56 |        "      <th></th>\n",
 57 |        "      <th>Hours</th>\n",
 58 |        "      <th>Scores</th>\n",
 59 |        "    </tr>\n",
 60 |        "  </thead>\n",
 61 |        "  <tbody>\n",
 62 |        "    <tr>\n",
 63 |        "      <th>0</th>\n",
 64 |        "      <td>2.5</td>\n",
 65 |        "      <td>21</td>\n",
 66 |        "    </tr>\n",
 67 |        "    <tr>\n",
 68 |        "      <th>1</th>\n",
 69 |        "      <td>5.1</td>\n",
 70 |        "      <td>47</td>\n",
 71 |        "    </tr>\n",
 72 |        "    <tr>\n",
 73 |        "      <th>2</th>\n",
 74 |        "      <td>3.2</td>\n",
 75 |        "      <td>27</td>\n",
 76 |        "    </tr>\n",
 77 |        "    <tr>\n",
 78 |        "      <th>3</th>\n",
 79 |        "      <td>8.5</td>\n",
 80 |        "      <td>75</td>\n",
 81 |        "    </tr>\n",
 82 |        "    <tr>\n",
 83 |        "      <th>4</th>\n",
 84 |        "      <td>3.5</td>\n",
 85 |        "      <td>30</td>\n",
 86 |        "    </tr>\n",
 87 |        "  </tbody>\n",
 88 |        "</table>\n",
 89 |        "</div>"
 90 |       ],
 91 |       "text/plain": [
 92 |        "   Hours  Scores\n",
 93 |        "0    2.5      21\n",
 94 |        "1    5.1      47\n",
 95 |        "2    3.2      27\n",
 96 |        "3    8.5      75\n",
 97 |        "4    3.5      30"
 98 |       ]
 99 |      },
100 |      "execution_count": 52,
101 |      "metadata": {},
102 |      "output_type": "execute_result"
103 |     }
104 |    ],
105 |    "source": [
106 |     "# Reading the Data \n",
107 |     "data = pd.read_csv('http://bit.ly/w-data')\n",
108 |     "data.head(5)"
109 |    ]
110 |   },
111 |   {
112 |    "cell_type": "code",
113 |    "execution_count": 53,
114 |    "metadata": {},
115 |    "outputs": [
116 |     {
117 |      "data": {
118 |       "text/plain": [
119 |        "False"
120 |       ]
121 |      },
122 |      "execution_count": 53,
123 |      "metadata": {},
124 |      "output_type": "execute_result"
125 |     }
126 |    ],
127 |    "source": [
128 |     "# Check if there any null value in the Dataset\n",
129 |     "data.isnull == True"
130 |    ]
131 |   },
132 |   {
133 |    "cell_type": "markdown",
134 |    "metadata": {},
135 |    "source": [
136 |     "**There is no null value in the Dataset so, we can now visualize our Data.**"
137 |    ]
138 |   },
139 |   {
140 |    "cell_type": "code",
141 |    "execution_count": 54,
142 |    "metadata": {},
143 |    "outputs": [
144 |     {
145 |      "data": {
146 |       "image/png": 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\n",
147 |       "text/plain": [
148 |        "<Figure size 432x288 with 1 Axes>"
149 |       ]
150 |      },
151 |      "metadata": {},
152 |      "output_type": "display_data"
153 |     }
154 |    ],
155 |    "source": [
156 |     "sns.set_style('darkgrid')\n",
157 |     "sns.scatterplot(y= data['Scores'], x= data['Hours'])\n",
158 |     "plt.title('Marks Vs Study Hours',size=20)\n",
159 |     "plt.ylabel('Marks Percentage', size=12)\n",
160 |     "plt.xlabel('Hours Studied', size=12)\n",
161 |     "plt.show()"
162 |    ]
163 |   },
164 |   {
165 |    "cell_type": "markdown",
166 |    "metadata": {},
167 |    "source": [
168 |     "**From the above scatter plot there looks to be correlation between the 'Marks Percentage' and 'Hours Studied', Lets plot a regression line to confirm the correlation.**"
169 |    ]
170 |   },
171 |   {
172 |    "cell_type": "code",
173 |    "execution_count": 55,
174 |    "metadata": {},
175 |    "outputs": [
176 |     {
177 |      "data": {
178 |       "image/png": 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\n",
179 |       "text/plain": [
180 |        "<Figure size 432x288 with 1 Axes>"
181 |       ]
182 |      },
183 |      "metadata": {},
184 |      "output_type": "display_data"
185 |     },
186 |     {
187 |      "name": "stdout",
188 |      "output_type": "stream",
189 |      "text": [
190 |       "           Hours    Scores\n",
191 |       "Hours   1.000000  0.976191\n",
192 |       "Scores  0.976191  1.000000\n"
193 |      ]
194 |     }
195 |    ],
196 |    "source": [
197 |     "sns.regplot(x= data['Hours'], y= data['Scores'])\n",
198 |     "plt.title('Regression Plot',size=20)\n",
199 |     "plt.ylabel('Marks Percentage', size=12)\n",
200 |     "plt.xlabel('Hours Studied', size=12)\n",
201 |     "plt.show()\n",
202 |     "print(data.corr())"
203 |    ]
204 |   },
205 |   {
206 |    "cell_type": "markdown",
207 |    "metadata": {},
208 |    "source": [
209 |     "**It is confirmed that the variables are positively correlated.**"
210 |    ]
211 |   },
212 |   {
213 |    "cell_type": "markdown",
214 |    "metadata": {},
215 |    "source": [
216 |     "## Training the Model\n",
217 |     "### 1) Splitting the Data"
218 |    ]
219 |   },
220 |   {
221 |    "cell_type": "code",
222 |    "execution_count": 56,
223 |    "metadata": {},
224 |    "outputs": [],
225 |    "source": [
226 |     "# Defining X and y from the Data\n",
227 |     "X = data.iloc[:, :-1].values  \n",
228 |     "y = data.iloc[:, 1].values\n",
229 |     "\n",
230 |     "# Spliting the Data in two\n",
231 |     "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state = 0)"
232 |    ]
233 |   },
234 |   {
235 |    "cell_type": "markdown",
236 |    "metadata": {},
237 |    "source": [
238 |     "### 2) Fitting the Data into the model"
239 |    ]
240 |   },
241 |   {
242 |    "cell_type": "code",
243 |    "execution_count": 57,
244 |    "metadata": {},
245 |    "outputs": [
246 |     {
247 |      "name": "stdout",
248 |      "output_type": "stream",
249 |      "text": [
250 |       "---------Model Trained---------\n"
251 |      ]
252 |     }
253 |    ],
254 |    "source": [
255 |     "regression = LinearRegression()\n",
256 |     "regression.fit(train_X, train_y)\n",
257 |     "print(\"---------Model Trained---------\")"
258 |    ]
259 |   },
260 |   {
261 |    "cell_type": "markdown",
262 |    "metadata": {},
263 |    "source": [
264 |     "## Predicting the Percentage of Marks"
265 |    ]
266 |   },
267 |   {
268 |    "cell_type": "code",
269 |    "execution_count": 58,
270 |    "metadata": {},
271 |    "outputs": [
272 |     {
273 |      "data": {
274 |       "text/html": [
275 |        "<div>\n",
276 |        "<style scoped>\n",
277 |        "    .dataframe tbody tr th:only-of-type {\n",
278 |        "        vertical-align: middle;\n",
279 |        "    }\n",
280 |        "\n",
281 |        "    .dataframe tbody tr th {\n",
282 |        "        vertical-align: top;\n",
283 |        "    }\n",
284 |        "\n",
285 |        "    .dataframe thead th {\n",
286 |        "        text-align: right;\n",
287 |        "    }\n",
288 |        "</style>\n",
289 |        "<table border=\"1\" class=\"dataframe\">\n",
290 |        "  <thead>\n",
291 |        "    <tr style=\"text-align: right;\">\n",
292 |        "      <th></th>\n",
293 |        "      <th>Hours</th>\n",
294 |        "      <th>Predicted Marks</th>\n",
295 |        "    </tr>\n",
296 |        "  </thead>\n",
297 |        "  <tbody>\n",
298 |        "    <tr>\n",
299 |        "      <th>0</th>\n",
300 |        "      <td>1.5</td>\n",
301 |        "      <td>16.844722</td>\n",
302 |        "    </tr>\n",
303 |        "    <tr>\n",
304 |        "      <th>1</th>\n",
305 |        "      <td>3.2</td>\n",
306 |        "      <td>33.745575</td>\n",
307 |        "    </tr>\n",
308 |        "    <tr>\n",
309 |        "      <th>2</th>\n",
310 |        "      <td>7.4</td>\n",
311 |        "      <td>75.500624</td>\n",
312 |        "    </tr>\n",
313 |        "    <tr>\n",
314 |        "      <th>3</th>\n",
315 |        "      <td>2.5</td>\n",
316 |        "      <td>26.786400</td>\n",
317 |        "    </tr>\n",
318 |        "    <tr>\n",
319 |        "      <th>4</th>\n",
320 |        "      <td>5.9</td>\n",
321 |        "      <td>60.588106</td>\n",
322 |        "    </tr>\n",
323 |        "    <tr>\n",
324 |        "      <th>5</th>\n",
325 |        "      <td>3.8</td>\n",
326 |        "      <td>39.710582</td>\n",
327 |        "    </tr>\n",
328 |        "    <tr>\n",
329 |        "      <th>6</th>\n",
330 |        "      <td>1.9</td>\n",
331 |        "      <td>20.821393</td>\n",
332 |        "    </tr>\n",
333 |        "  </tbody>\n",
334 |        "</table>\n",
335 |        "</div>"
336 |       ],
337 |       "text/plain": [
338 |        "   Hours  Predicted Marks\n",
339 |        "0    1.5        16.844722\n",
340 |        "1    3.2        33.745575\n",
341 |        "2    7.4        75.500624\n",
342 |        "3    2.5        26.786400\n",
343 |        "4    5.9        60.588106\n",
344 |        "5    3.8        39.710582\n",
345 |        "6    1.9        20.821393"
346 |       ]
347 |      },
348 |      "execution_count": 58,
349 |      "metadata": {},
350 |      "output_type": "execute_result"
351 |     }
352 |    ],
353 |    "source": [
354 |     "pred_y = regression.predict(val_X)\n",
355 |     "prediction = pd.DataFrame({'Hours': [i[0] for i in val_X], 'Predicted Marks': [k for k in pred_y]})\n",
356 |     "prediction"
357 |    ]
358 |   },
359 |   {
360 |    "cell_type": "markdown",
361 |    "metadata": {},
362 |    "source": [
363 |     "## Comparing the Predicted Marks with the Actual Marks"
364 |    ]
365 |   },
366 |   {
367 |    "cell_type": "code",
368 |    "execution_count": 59,
369 |    "metadata": {},
370 |    "outputs": [
371 |     {
372 |      "data": {
373 |       "text/html": [
374 |        "<div>\n",
375 |        "<style scoped>\n",
376 |        "    .dataframe tbody tr th:only-of-type {\n",
377 |        "        vertical-align: middle;\n",
378 |        "    }\n",
379 |        "\n",
380 |        "    .dataframe tbody tr th {\n",
381 |        "        vertical-align: top;\n",
382 |        "    }\n",
383 |        "\n",
384 |        "    .dataframe thead th {\n",
385 |        "        text-align: right;\n",
386 |        "    }\n",
387 |        "</style>\n",
388 |        "<table border=\"1\" class=\"dataframe\">\n",
389 |        "  <thead>\n",
390 |        "    <tr style=\"text-align: right;\">\n",
391 |        "      <th></th>\n",
392 |        "      <th>Actual Marks</th>\n",
393 |        "      <th>Predicted Marks</th>\n",
394 |        "    </tr>\n",
395 |        "  </thead>\n",
396 |        "  <tbody>\n",
397 |        "    <tr>\n",
398 |        "      <th>0</th>\n",
399 |        "      <td>20</td>\n",
400 |        "      <td>16.844722</td>\n",
401 |        "    </tr>\n",
402 |        "    <tr>\n",
403 |        "      <th>1</th>\n",
404 |        "      <td>27</td>\n",
405 |        "      <td>33.745575</td>\n",
406 |        "    </tr>\n",
407 |        "    <tr>\n",
408 |        "      <th>2</th>\n",
409 |        "      <td>69</td>\n",
410 |        "      <td>75.500624</td>\n",
411 |        "    </tr>\n",
412 |        "    <tr>\n",
413 |        "      <th>3</th>\n",
414 |        "      <td>30</td>\n",
415 |        "      <td>26.786400</td>\n",
416 |        "    </tr>\n",
417 |        "    <tr>\n",
418 |        "      <th>4</th>\n",
419 |        "      <td>62</td>\n",
420 |        "      <td>60.588106</td>\n",
421 |        "    </tr>\n",
422 |        "    <tr>\n",
423 |        "      <th>5</th>\n",
424 |        "      <td>35</td>\n",
425 |        "      <td>39.710582</td>\n",
426 |        "    </tr>\n",
427 |        "    <tr>\n",
428 |        "      <th>6</th>\n",
429 |        "      <td>24</td>\n",
430 |        "      <td>20.821393</td>\n",
431 |        "    </tr>\n",
432 |        "  </tbody>\n",
433 |        "</table>\n",
434 |        "</div>"
435 |       ],
436 |       "text/plain": [
437 |        "   Actual Marks  Predicted Marks\n",
438 |        "0            20        16.844722\n",
439 |        "1            27        33.745575\n",
440 |        "2            69        75.500624\n",
441 |        "3            30        26.786400\n",
442 |        "4            62        60.588106\n",
443 |        "5            35        39.710582\n",
444 |        "6            24        20.821393"
445 |       ]
446 |      },
447 |      "execution_count": 59,
448 |      "metadata": {},
449 |      "output_type": "execute_result"
450 |     }
451 |    ],
452 |    "source": [
453 |     "compare_scores = pd.DataFrame({'Actual Marks': val_y, 'Predicted Marks': pred_y})\n",
454 |     "compare_scores"
455 |    ]
456 |   },
457 |   {
458 |    "cell_type": "markdown",
459 |    "metadata": {},
460 |    "source": [
461 |     "## Visually Comparing the Predicted Marks with the Actual Marks"
462 |    ]
463 |   },
464 |   {
465 |    "cell_type": "code",
466 |    "execution_count": 60,
467 |    "metadata": {},
468 |    "outputs": [
469 |     {
470 |      "data": {
471 |       "image/png": 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\n",
472 |       "text/plain": [
473 |        "<Figure size 432x288 with 1 Axes>"
474 |       ]
475 |      },
476 |      "metadata": {},
477 |      "output_type": "display_data"
478 |     }
479 |    ],
480 |    "source": [
481 |     "plt.scatter(x=val_X, y=val_y, color='blue')\n",
482 |     "plt.plot(val_X, pred_y, color='Black')\n",
483 |     "plt.title('Actual vs Predicted', size=20)\n",
484 |     "plt.ylabel('Marks Percentage', size=12)\n",
485 |     "plt.xlabel('Hours Studied', size=12)\n",
486 |     "plt.show()"
487 |    ]
488 |   },
489 |   {
490 |    "cell_type": "markdown",
491 |    "metadata": {},
492 |    "source": [
493 |     "## Evaluating the Model"
494 |    ]
495 |   },
496 |   {
497 |    "cell_type": "code",
498 |    "execution_count": 61,
499 |    "metadata": {},
500 |    "outputs": [
501 |     {
502 |      "name": "stdout",
503 |      "output_type": "stream",
504 |      "text": [
505 |       "Mean absolute error:  4.130879918502486\n"
506 |      ]
507 |     }
508 |    ],
509 |    "source": [
510 |     "# Calculating the accuracy of the model\n",
511 |     "print('Mean absolute error: ',mean_absolute_error(val_y,pred_y))"
512 |    ]
513 |   },
514 |   {
515 |    "cell_type": "markdown",
516 |    "metadata": {},
517 |    "source": [
518 |     "**Small value of Mean absolute error states that the chances of error or wrong forecasting through the model are very less.**"
519 |    ]
520 |   },
521 |   {
522 |    "cell_type": "markdown",
523 |    "metadata": {},
524 |    "source": [
525 |     "## What will be the predicted score of a student if he/she studies for 9.25 hrs/ day?"
526 |    ]
527 |   },
528 |   {
529 |    "cell_type": "code",
530 |    "execution_count": 62,
531 |    "metadata": {},
532 |    "outputs": [
533 |     {
534 |      "name": "stdout",
535 |      "output_type": "stream",
536 |      "text": [
537 |       "Score = 93.893\n"
538 |      ]
539 |     }
540 |    ],
541 |    "source": [
542 |     "hours = [9.25]\n",
543 |     "answer = regression.predict([hours])\n",
544 |     "print(\"Score = {}\".format(round(answer[0],3)))"
545 |    ]
546 |   },
547 |   {
548 |    "cell_type": "markdown",
549 |    "metadata": {},
550 |    "source": [
551 |     "**According to the regression model if a student studies for 9.25 hours a day he/she is likely to score 93.89 marks.**"
552 |    ]
553 |   }
554 |  ],
555 |  "metadata": {
556 |   "kernelspec": {
557 |    "display_name": "Python 3",
558 |    "language": "python",
559 |    "name": "python3"
560 |   },
561 |   "language_info": {
562 |    "codemirror_mode": {
563 |     "name": "ipython",
564 |     "version": 3
565 |    },
566 |    "file_extension": ".py",
567 |    "mimetype": "text/x-python",
568 |    "name": "python",
569 |    "nbconvert_exporter": "python",
570 |    "pygments_lexer": "ipython3",
571 |    "version": "3.8.3"
572 |   }
573 |  },
574 |  "nbformat": 4,
575 |  "nbformat_minor": 4
576 | }
577 | 


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