├── Lab-2
    ├── Thyroid.ipynb
    ├── Thyroid2 (1).ipynb
    ├── Thyroid2.ipynb
    ├── kali
    └── lab_2 (1).ipynb
├── Lab-3
    ├── Lab03
    ├── Lab3-comm.ipynb
    └── Lab3.ipynb
├── Lab-4
    ├── Lab-4-comm.ipynb
    ├── Lab-4-updated.ipynb
    ├── Lab-4.ipynb
    └── ss
├── Lab-5
    ├── Lab-5 (1).ipynb
    ├── Lab-5.ipynb
    └── sss
├── Lab-7
    ├── Lab-7.ipynb
    └── ss
├── Lab6
    ├── Lab-6 (1).ipynb
    └── ss
├── Lab_1 (1).ipynb
└── lab_2.ipynb


/Lab-2/kali:
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1 | 
2 | 


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/Lab-2/lab_2 (1).ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 18,
  6 |    "metadata": {
  7 |     "colab": {
  8 |      "base_uri": "https://localhost:8080/"
  9 |     },
 10 |     "id": "uzfbpsfgPudm",
 11 |     "outputId": "c65f5337-21a7-4511-bca2-1892d49eed9e"
 12 |    },
 13 |    "outputs": [
 14 |     {
 15 |      "name": "stdout",
 16 |      "output_type": "stream",
 17 |      "text": [
 18 |       "Dimensionality of the vector space: 3\n",
 19 |       "Number of vectors in the vector space: 10\n",
 20 |       "Rank of Matrix A: 3\n",
 21 |       "\n",
 22 |       "Matrix A:\n",
 23 |       "[[20  6  2]\n",
 24 |       " [16  3  6]\n",
 25 |       " [27  6  2]\n",
 26 |       " [19  1  2]\n",
 27 |       " [24  4  2]\n",
 28 |       " [22  1  5]\n",
 29 |       " [15  4  2]\n",
 30 |       " [18  4  2]\n",
 31 |       " [21  1  4]\n",
 32 |       " [16  2  4]]\n",
 33 |       "\n",
 34 |       "Matrix C:\n",
 35 |       "[[386]\n",
 36 |       " [289]\n",
 37 |       " [393]\n",
 38 |       " [110]\n",
 39 |       " [280]\n",
 40 |       " [167]\n",
 41 |       " [271]\n",
 42 |       " [274]\n",
 43 |       " [148]\n",
 44 |       " [198]]\n",
 45 |       "\n",
 46 |       "Pseudo-Inverse of A:\n",
 47 |       "[[-0.01008596 -0.03124505  0.01013951  0.0290728   0.0182907   0.01161794\n",
 48 |       "  -0.00771348  0.00095458  0.01743623 -0.00542016]\n",
 49 |       " [ 0.09059668  0.07263726  0.03172933 -0.09071908 -0.01893196 -0.06926996\n",
 50 |       "   0.05675464  0.03152577 -0.07641966  0.00357352]\n",
 51 |       " [ 0.00299878  0.15874243 -0.05795468 -0.06609024 -0.06295043  0.03348017\n",
 52 |       "   0.01541831 -0.01070461  0.00029003  0.05938755]]\n",
 53 |       "\n",
 54 |       "Cost of each product available for sale (Matrix X):\n",
 55 |       "[[ 1.]\n",
 56 |       " [55.]\n",
 57 |       " [18.]]\n"
 58 |      ]
 59 |     }
 60 |    ],
 61 |    "source": [
 62 |     "import numpy as np\n",
 63 |     "import pandas as pd\n",
 64 |     "\n",
 65 |     "df = pd.read_excel('Lab Session Data.xlsx', sheet_name=0)\n",
 66 |     "df = df.iloc[:,:5]\n",
 67 |     "A = df[['Candies (#)', 'Mangoes (Kg)', 'Milk Packets (#)']].to_numpy()\n",
 68 |     "C = df['Payment (Rs)'].to_numpy()\n",
 69 |     "\n",
 70 |     "C = C.reshape(-1, 1)\n",
 71 |     "\n",
 72 |     "dimensionality = A.shape[1]\n",
 73 |     "print(f\"Dimensionality of the vector space: {dimensionality}\")\n",
 74 |     "\n",
 75 |     "num_vectors = A.shape[0]\n",
 76 |     "print(f\"Number of vectors in the vector space: {num_vectors}\")\n",
 77 |     "\n",
 78 |     "rank_A = np.linalg.matrix_rank(A)\n",
 79 |     "print(f\"Rank of Matrix A: {rank_A}\")\n",
 80 |     "\n",
 81 |     "A_pseudo_inv = np.linalg.pinv(A)\n",
 82 |     "\n",
 83 |     "X = A_pseudo_inv @ C\n",
 84 |     "\n",
 85 |     "print(\"\\nMatrix A:\")\n",
 86 |     "print(A)\n",
 87 |     "\n",
 88 |     "print(\"\\nMatrix C:\")\n",
 89 |     "print(C)\n",
 90 |     "\n",
 91 |     "print(\"\\nPseudo-Inverse of A:\")\n",
 92 |     "print(A_pseudo_inv)\n",
 93 |     "\n",
 94 |     "print(\"\\nCost of each product available for sale (Matrix X):\")\n",
 95 |     "print(X)\n"
 96 |    ]
 97 |   },
 98 |   {
 99 |    "cell_type": "code",
100 |    "execution_count": 19,
101 |    "metadata": {
102 |     "id": "miX7qSvFQVS4"
103 |    },
104 |    "outputs": [],
105 |    "source": [
106 |     "df['Class'] = np.where(df['Payment (Rs)'] > 200, 'RICH', 'POOR')"
107 |    ]
108 |   },
109 |   {
110 |    "cell_type": "code",
111 |    "execution_count": 20,
112 |    "metadata": {},
113 |    "outputs": [
114 |     {
115 |      "name": "stdout",
116 |      "output_type": "stream",
117 |      "text": [
118 |       "  Customer  Candies (#)  Mangoes (Kg)  Milk Packets (#)  Payment (Rs) Class\n",
119 |       "0      C_1           20             6                 2           386  RICH\n",
120 |       "1      C_2           16             3                 6           289  RICH\n",
121 |       "2      C_3           27             6                 2           393  RICH\n",
122 |       "3      C_4           19             1                 2           110  POOR\n",
123 |       "4      C_5           24             4                 2           280  RICH\n",
124 |       "5      C_6           22             1                 5           167  POOR\n",
125 |       "6      C_7           15             4                 2           271  RICH\n",
126 |       "7      C_8           18             4                 2           274  RICH\n",
127 |       "8      C_9           21             1                 4           148  POOR\n",
128 |       "9     C_10           16             2                 4           198  POOR\n"
129 |      ]
130 |     }
131 |    ],
132 |    "source": [
133 |     "print(df)"
134 |    ]
135 |   },
136 |   {
137 |    "cell_type": "code",
138 |    "execution_count": 21,
139 |    "metadata": {},
140 |    "outputs": [
141 |     {
142 |      "name": "stdout",
143 |      "output_type": "stream",
144 |      "text": [
145 |       "Mean Price: 1560.663453815261\n",
146 |       "Variance Price: 58732.365352539186\n"
147 |      ]
148 |     }
149 |    ],
150 |    "source": [
151 |     "#4Q\n",
152 |     "#i)\n",
153 |     "import pandas as pd\n",
154 |     "import statistics\n",
155 |     "\n",
156 |     "\n",
157 |     "df = pd.read_excel('Lab Session Data.xlsx', sheet_name=1)\n",
158 |     "\n",
159 |     "\n",
160 |     "prices = df['Price']\n",
161 |     "\n",
162 |     "\n",
163 |     "mean_price = statistics.mean(prices)\n",
164 |     "variance_price = statistics.variance(prices)\n",
165 |     "\n",
166 |     "print(f\"Mean Price: {mean_price}\")\n",
167 |     "print(f\"Variance Price: {variance_price}\")\n"
168 |    ]
169 |   },
170 |   {
171 |    "cell_type": "code",
172 |    "execution_count": 22,
173 |    "metadata": {},
174 |    "outputs": [
175 |     {
176 |      "name": "stdout",
177 |      "output_type": "stream",
178 |      "text": [
179 |       "Mean Price on Wednesdays: 1550.706\n",
180 |       "Population Mean Price: 1560.663453815261\n"
181 |      ]
182 |     }
183 |    ],
184 |    "source": [
185 |     "#4Q\n",
186 |     "#ii)\n",
187 |     "wednesdays = df[df['Day'] == 'Wed']\n",
188 |     "wednesday_prices = wednesdays['Price']\n",
189 |     "\n",
190 |     "mean_wednesday_price = statistics.mean(wednesday_prices)\n",
191 |     "\n",
192 |     "print(f\"Mean Price on Wednesdays: {mean_wednesday_price}\")\n",
193 |     "print(f\"Population Mean Price: {mean_price}\")\n"
194 |    ]
195 |   },
196 |   {
197 |    "cell_type": "code",
198 |    "execution_count": 23,
199 |    "metadata": {},
200 |    "outputs": [
201 |     {
202 |      "name": "stdout",
203 |      "output_type": "stream",
204 |      "text": [
205 |       "Mean Price in April: 1698.9526315789474\n",
206 |       "Population Mean Price: 1560.663453815261\n"
207 |      ]
208 |     }
209 |    ],
210 |    "source": [
211 |     "#4Q\n",
212 |     "#iii)\n",
213 |     "# Filter for April\n",
214 |     "april_data = df[df['Month'] == 'Apr']\n",
215 |     "april_prices = april_data['Price']\n",
216 |     "\n",
217 |     "# Calculate sample mean for April\n",
218 |     "mean_april_price = statistics.mean(april_prices)\n",
219 |     "\n",
220 |     "print(f\"Mean Price in April: {mean_april_price}\")\n",
221 |     "print(f\"Population Mean Price: {mean_price}\")\n"
222 |    ]
223 |   },
224 |   {
225 |    "cell_type": "code",
226 |    "execution_count": 24,
227 |    "metadata": {},
228 |    "outputs": [
229 |     {
230 |      "name": "stdout",
231 |      "output_type": "stream",
232 |      "text": [
233 |       "Probability of Making a Loss: 0.4979919678714859\n"
234 |      ]
235 |     }
236 |    ],
237 |    "source": [
238 |     "#4Q\n",
239 |     "#iV)\n",
240 |     "df['Chg%'] = df['Chg%'].astype(float) / 100\n",
241 |     "loss_prob = len(df[df['Chg%'] < 0]) / len(df)\n",
242 |     "\n",
243 |     "print(f\"Probability of Making a Loss: {loss_prob}\")\n"
244 |    ]
245 |   },
246 |   {
247 |    "cell_type": "code",
248 |    "execution_count": 25,
249 |    "metadata": {},
250 |    "outputs": [
251 |     {
252 |      "name": "stdout",
253 |      "output_type": "stream",
254 |      "text": [
255 |       "Probability of Making a Profit on Wednesdays: 0.42\n"
256 |      ]
257 |     }
258 |    ],
259 |    "source": [
260 |     "#4Q\n",
261 |     "#V)\n",
262 |     "wednesday_chg = wednesdays['Chg%']\n",
263 |     "\n",
264 |     "profit_prob_wed = len(wednesday_chg[wednesday_chg > 0]) / len(wednesday_chg)\n",
265 |     "\n",
266 |     "print(f\"Probability of Making a Profit on Wednesdays: {profit_prob_wed}\")\n"
267 |    ]
268 |   },
269 |   {
270 |    "cell_type": "code",
271 |    "execution_count": 26,
272 |    "metadata": {},
273 |    "outputs": [
274 |     {
275 |      "name": "stdout",
276 |      "output_type": "stream",
277 |      "text": [
278 |       "Conditional Probability of Making a Profit Given that Today is Wednesday: 0.42\n"
279 |      ]
280 |     }
281 |    ],
282 |    "source": [
283 |     "#4Q\n",
284 |     "#Vi)\n",
285 |     "\n",
286 |     "wednesday_data = df[df['Day'] == 'Wed']\n",
287 |     "\n",
288 |     "\n",
289 |     "wednesday_count = len(wednesday_data)\n",
290 |     "wednesday_profit_count = len(wednesday_data[wednesday_data['Chg%'] > 0])\n",
291 |     "\n",
292 |     "conditional_prob = wednesday_profit_count / wednesday_count\n",
293 |     "\n",
294 |     "print(f\"Conditional Probability of Making a Profit Given that Today is Wednesday: {conditional_prob}\")\n"
295 |    ]
296 |   },
297 |   {
298 |    "cell_type": "code",
299 |    "execution_count": 27,
300 |    "metadata": {},
301 |    "outputs": [
302 |     {
303 |      "data": {
304 |       "image/png": 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",
305 |       "text/plain": [
306 |        "<Figure size 640x480 with 1 Axes>"
307 |       ]
308 |      },
309 |      "metadata": {},
310 |      "output_type": "display_data"
311 |     }
312 |    ],
313 |    "source": [
314 |     "#4Q\n",
315 |     "#vii)\n",
316 |     "import matplotlib.pyplot as plt\n",
317 |     "\n",
318 |     "day_mapping = {'Mon': 0, 'Tue': 1, 'Wed': 2, 'Thu': 3, 'Fri': 4}\n",
319 |     "df['Day_num'] = df['Day'].map(day_mapping)\n",
320 |     "\n",
321 |     "plt.scatter(df['Day_num'], df['Chg%'])\n",
322 |     "plt.xlabel('Day of the Week')\n",
323 |     "plt.ylabel('Chg%')\n",
324 |     "plt.title('Scatter Plot of Chg% Data Against the Day of the Week')\n",
325 |     "plt.xticks(ticks=list(day_mapping.values()), labels=list(day_mapping.keys()))\n",
326 |     "plt.grid(True)\n",
327 |     "plt.show()\n"
328 |    ]
329 |   },
330 |   {
331 |    "cell_type": "code",
332 |    "execution_count": null,
333 |    "metadata": {},
334 |    "outputs": [],
335 |    "source": []
336 |   }
337 |  ],
338 |  "metadata": {
339 |   "colab": {
340 |    "provenance": []
341 |   },
342 |   "kernelspec": {
343 |    "display_name": "Python 3 (ipykernel)",
344 |    "language": "python",
345 |    "name": "python3"
346 |   },
347 |   "language_info": {
348 |    "codemirror_mode": {
349 |     "name": "ipython",
350 |     "version": 3
351 |    },
352 |    "file_extension": ".py",
353 |    "mimetype": "text/x-python",
354 |    "name": "python",
355 |    "nbconvert_exporter": "python",
356 |    "pygments_lexer": "ipython3",
357 |    "version": "3.11.7"
358 |   }
359 |  },
360 |  "nbformat": 4,
361 |  "nbformat_minor": 4
362 | }
363 | 


--------------------------------------------------------------------------------
/Lab-3/Lab03:
--------------------------------------------------------------------------------
 1 | Subject: 19CSE305
 2 | Lab Session: 03
 3 | Notes:
 4 | 1. Please read the assignment notes carefully and comply to the guidelines provided.
 5 | 2. Code should be checked into the GitHub. These details shall be provided in the Lab.
 6 | 3. If you have not completed the prerequisite assignments, please complete them before the 
 7 | next lab session.
 8 | Coding Instructions:
 9 | 1. The code should be modularized; The asked functionality should be available as a function. 
10 | Please create multiple functions if needed. However, all functions should be present within a 
11 | single code block, if you are using Jupyter or Colab notebooks.
12 | 2. There should be no print statement within the function. All print statements should be in the 
13 | main program.
14 | 3. Please use proper naming of variables.
15 | 4. For lists, strings and matrices, you may use your input values as appropriate.
16 | 5. Please make inline documentation / comments as needed within the code blocks.
17 | Main Section (Mandatory):
18 | Please use the data associated with your own project. This assignment deals with classification 
19 | models.
20 | For dot product → use numpy.dot()
21 | For Vector length → use numpy.linalg.norm()
22 | Refer to lecture portions on k-NN. Also refer: 
23 | https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
24 | Please use help manuals of sklearn package to gain understanding of the model behaviors as well as 
25 | ways to use various package functionalities.
26 | A1. Evaluate the intraclass spread and interclass distances between the classes in your dataset. If 
27 | your data deals with multiple classes, you can take any two classes. Steps below (refer below 
28 | diagram for understanding):
29 | • Calculate the mean for each class (also called as class centroid)
30 | (Suggestion: You may use numpy.mean() function for finding the average vector for all 
31 | vectors in a given class. Please define the axis property appropriately to use this function. EX: 
32 | feat_vecs.mean(axis=0))
33 | • Calculate spread (standard deviation) for each class
34 | (Suggestion: You may use numpy.std() function for finding the standard deviation vector 
35 | for all vectors in a given class. Please define the axis property appropriately to use this 
36 | function.)
37 | • Calculate the distance between mean vectors between classes
38 | (Suggestion: numpy.linalg.norm(centroid1 – centroid2) gives the Euclidean 
39 | distance between two centroids.)
40 | A2. Take any feature from your dataset. Observe the density pattern for that feature by plotting the 
41 | histogram. Use buckets (data in ranges) for histogram generation and study. Calculate the mean and 
42 | variance from the available data. 
43 | (Suggestion: numpy.histogram()gives the histogram data. Plot of histogram may be 
44 | achieved with matplotlib.pyplot.hist())
45 | A3. Take any two feature vectors from your dataset. Calculate the Minkwoski distance with r from 1 
46 | to 10. Make a plot of the distance and observe the nature of this graph.
47 | A4. Divide dataset in your project into two parts – train & test set. To accomplish this, use the traintest_split() function available in SciKit. See below sample code for help:
48 | >>> import numpy as np
49 | >>> from sklearn.model_selection import train_test_split
50 | >>> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
51 | X is the feature vector set for your project and y is the class levels for vectors present in X.
52 | Note: Before set split, make sure you have only two classes. If your project deals with multi-class 
53 | problem, take any two classes from them.
54 | A5. Train a kNN classifier (k =3) using the training set obtained from above exercise. Following code 
55 | for help:
56 | >>> import numpy as np
57 | >>> from sklearn.neighbors import KNeighborsClassifier
58 | >>> neigh = KNeighborsClassifier(n_neighbors=3)
59 | >>> neigh.fit(X, y)
60 | A6. Test the accuracy of the kNN using the test set obtained from above exercise. Following code for 
61 | help.
62 | >>> neigh.score(X_test, y_test)
63 | This code shall generate an accuracy report for you. Please study the report and understand it.
64 | A7. Use the predict() function to study the prediction behavior of the classifier for test vectors.
65 | >>> neigh.predict(X_test)
66 | Perform classification for a given vector using neigh.predict(<<test_vect>>). This shall produce the 
67 | class of the test vector (test_vect is any feature vector from your test set).
68 | A8. Make k = 1 to implement NN classifier and compare the results with kNN (k = 3). Vary k from 1 to 
69 | 11 and make an accuracy plot.
70 | A9. Please evaluate confusion matrix for your classification problem. From confusion matrix, the 
71 | other performance metrics such as precision, recall and F1-Score measures for both training and test 
72 | data. Based on your observations, infer the models learning outcome (underfit / regularfit / overfit).
73 | Optional Section:
74 | O1. Create a normal distribution data, plot the graph and compare the normal distribution plot 
75 | against the histogram plot.
76 | O2. Use different distance metric for kNN classifier by tuning the metric parameters of 
77 | KNeighborsClassifier(). Observe the behaviour with change in the distance for classification.
78 | O3. Make an AUROC plot for your project for kNN classifier. Compare the results with the area 
79 | obtained and infer. 
80 | O4. Compare the performance of your developed kNN classifier (during 2nd Lab exercise) with that 
81 | of the package provided model.
82 | Report Assignment:
83 | 1. Update your understanding of your project in the introduction section of the report.
84 | 2. Study the downloaded papers & update the literature survey section of your report. 
85 | 3. Expand the methodology and results sections with outcomes of this experiments & results 
86 | obtained. Please discuss your observations, inferences in results & discussion section. Please 
87 | conclude the report appropriately with these experiments. Consider following points for 
88 | observation analysis & inferences.
89 | • Do you think the classes you have in your dataset are well separated? Justify your 
90 | answer.
91 | • Explain the behavior of the kNN classifier with increase in value of k. Explain the 
92 | scenarios of over-fitting and under-fitting in kNN classifier. 
93 | • Do you think the kNN classifier is a good classifier based on the results obtained on 
94 | various metrics? 
95 | • Do you think the model has regular fit situation? Use train and test set performances to 
96 | arrive at this inference. 
97 | • When do you think a situation of overfit happens for kNN classifier
98 | 


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1 | lol
2 | 


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/Lab-5/Lab-5.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 7,
  6 |    "id": "4cedc66f-fcf4-4fc5-8274-6396ef3fe77f",
  7 |    "metadata": {},
  8 |    "outputs": [
  9 |     {
 10 |      "name": "stdout",
 11 |      "output_type": "stream",
 12 |      "text": [
 13 |       "   Header_and_Main_Declaration  Incomprehensible_Code  \\\n",
 14 |       "0                            2                    2.0   \n",
 15 |       "1                            2                    2.0   \n",
 16 |       "2                            2                    2.0   \n",
 17 |       "3                            2                    1.0   \n",
 18 |       "4                            2                    2.0   \n",
 19 |       "\n",
 20 |       "   Comprehensible_Code_with_logical_errors  \\\n",
 21 |       "0                                        2   \n",
 22 |       "1                                        2   \n",
 23 |       "2                                        1   \n",
 24 |       "3                                        1   \n",
 25 |       "4                                        1   \n",
 26 |       "\n",
 27 |       "   Comprehensible_code_with_syntax_errors  Correct_code_and_output  \\\n",
 28 |       "0                                     2.0                        2   \n",
 29 |       "1                                     2.0                        0   \n",
 30 |       "2                                     1.0                        1   \n",
 31 |       "3                                     1.0                        0   \n",
 32 |       "4                                     1.0                        0   \n",
 33 |       "\n",
 34 |       "   Final_Marks  \n",
 35 |       "0           10  \n",
 36 |       "1            8  \n",
 37 |       "2            7  \n",
 38 |       "3            5  \n",
 39 |       "4            6  \n"
 40 |      ]
 41 |     }
 42 |    ],
 43 |    "source": [
 44 |     "#collecting and loading data\n",
 45 |     "import pandas as pd\n",
 46 |     "import numpy as np\n",
 47 |     "\n",
 48 |     "def read_data(file_name):\n",
 49 |     "    df = pd.read_excel(file_name)\n",
 50 |     "    df = df.fillna(df.mean(numeric_only=True))  # Fill missing values with column mean for numeric columns\n",
 51 |     "    df = df.iloc[:, 4:10]  # Select columns 4 to 9\n",
 52 |     "    return df\n",
 53 |     "\n",
 54 |     "df = read_data('ml-data.xlsx')\n",
 55 |     "\n",
 56 |     "print(df.head())\n"
 57 |    ]
 58 |   },
 59 |   {
 60 |    "cell_type": "code",
 61 |    "execution_count": 12,
 62 |    "id": "96218e3f-8c15-444a-84f4-dbfd81325108",
 63 |    "metadata": {},
 64 |    "outputs": [
 65 |     {
 66 |      "data": {
 67 |       "text/html": [
 68 |        "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
 69 |       ],
 70 |       "text/plain": [
 71 |        "LinearRegression()"
 72 |       ]
 73 |      },
 74 |      "execution_count": 12,
 75 |      "metadata": {},
 76 |      "output_type": "execute_result"
 77 |     }
 78 |    ],
 79 |    "source": [
 80 |     "#1q)\n",
 81 |     "from sklearn.model_selection import train_test_split\n",
 82 |     "from sklearn.linear_model import LinearRegression\n",
 83 |     "\n",
 84 |     "def train_test_splits(X, y):\n",
 85 |     "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
 86 |     "    return X_train, X_test, y_train, y_test\n",
 87 |     "\n",
 88 |     "def train_model(X_train, y_train):\n",
 89 |     "    reg = LinearRegression().fit(X_train, y_train)\n",
 90 |     "    y_train_pred = reg.predict(X_train)\n",
 91 |     "    return reg\n",
 92 |     "\n",
 93 |     "# Split the data into train and test\n",
 94 |     "X_train, X_test, y_train, y_test = train_test_splits(df[['Correct_code_and_output']], df['Final_Marks'])\n",
 95 |     "\n",
 96 |     "# Train the model\n",
 97 |     "model = train_model(X_train, y_train)\n",
 98 |     "model"
 99 |    ]
100 |   },
101 |   {
102 |    "cell_type": "code",
103 |    "execution_count": 13,
104 |    "id": "7e2a7767-3016-404e-93ff-50b536f8f017",
105 |    "metadata": {},
106 |    "outputs": [
107 |     {
108 |      "name": "stdout",
109 |      "output_type": "stream",
110 |      "text": [
111 |       "Training Set Metrics:\n",
112 |       "MSE: 2.64, RMSE: 1.63, MAPE: 11096952697985274.00%, R²: 0.43\n",
113 |       "\n",
114 |       "Testing Set Metrics:\n",
115 |       "MSE: 2.74, RMSE: 1.65, MAPE: 17679890739162960.00%, R²: 0.36\n"
116 |      ]
117 |     }
118 |    ],
119 |    "source": [
120 |     "#2q)\n",
121 |     "\n",
122 |     "from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error, r2_score\n",
123 |     "import numpy as np\n",
124 |     "\n",
125 |     "# Function to evaluate the model's performance\n",
126 |     "def evaluate_model(y_true, y_pred):\n",
127 |     "    mse = mean_squared_error(y_true, y_pred)\n",
128 |     "    rmse = np.sqrt(mse)\n",
129 |     "    mape = mean_absolute_percentage_error(y_true, y_pred)\n",
130 |     "    r2 = r2_score(y_true, y_pred)\n",
131 |     "    return mse, rmse, mape, r2\n",
132 |     "\n",
133 |     "\n",
134 |     "y_train_pred = model.predict(X_train)\n",
135 |     "y_test_pred = model.predict(X_test)\n",
136 |     "\n",
137 |     "# for the training set\n",
138 |     "train_mse, train_rmse, train_mape, train_r2 = evaluate_model(y_train, y_train_pred)\n",
139 |     "\n",
140 |     "# for the testing set\n",
141 |     "test_mse, test_rmse, test_mape, test_r2 = evaluate_model(y_test, y_test_pred)\n",
142 |     "\n",
143 |     "print(\"Training Set Metrics:\")\n",
144 |     "print(f\"MSE: {train_mse:.2f}, RMSE: {train_rmse:.2f}, MAPE: {train_mape:.2%}, R²: {train_r2:.2f}\")\n",
145 |     "\n",
146 |     "print(\"\\nTesting Set Metrics:\")\n",
147 |     "print(f\"MSE: {test_mse:.2f}, RMSE: {test_rmse:.2f}, MAPE: {test_mape:.2%}, R²: {test_r2:.2f}\")\n"
148 |    ]
149 |   },
150 |   {
151 |    "cell_type": "code",
152 |    "execution_count": 14,
153 |    "id": "1cb0afc5-5562-4a55-a870-48c32a6d75b4",
154 |    "metadata": {},
155 |    "outputs": [
156 |     {
157 |      "name": "stdout",
158 |      "output_type": "stream",
159 |      "text": [
160 |       "   Header_and_Main_Declaration  Incomprehensible_Code  \\\n",
161 |       "0                            2                    2.0   \n",
162 |       "1                            2                    2.0   \n",
163 |       "2                            2                    2.0   \n",
164 |       "3                            2                    1.0   \n",
165 |       "4                            2                    2.0   \n",
166 |       "\n",
167 |       "   Comprehensible_Code_with_logical_errors  \\\n",
168 |       "0                                        2   \n",
169 |       "1                                        2   \n",
170 |       "2                                        1   \n",
171 |       "3                                        1   \n",
172 |       "4                                        1   \n",
173 |       "\n",
174 |       "   Comprehensible_code_with_syntax_errors  Correct_code_and_output  \\\n",
175 |       "0                                     2.0                        2   \n",
176 |       "1                                     2.0                        0   \n",
177 |       "2                                     1.0                        1   \n",
178 |       "3                                     1.0                        0   \n",
179 |       "4                                     1.0                        0   \n",
180 |       "\n",
181 |       "   Final_Marks  \n",
182 |       "0           10  \n",
183 |       "1            8  \n",
184 |       "2            7  \n",
185 |       "3            5  \n",
186 |       "4            6  \n"
187 |      ]
188 |     }
189 |    ],
190 |    "source": [
191 |     "# reloading the data\n",
192 |     "\n",
193 |     "df = read_data('ml-data.xlsx')\n",
194 |     "\n",
195 |     "print(df.head())"
196 |    ]
197 |   },
198 |   {
199 |    "cell_type": "code",
200 |    "execution_count": 21,
201 |    "id": "83f1c4fd-4699-47bc-879c-9e89b062cb77",
202 |    "metadata": {},
203 |    "outputs": [
204 |     {
205 |      "name": "stdout",
206 |      "output_type": "stream",
207 |      "text": [
208 |       "Training Set Metrics:\n",
209 |       "MSE: 0.00, RMSE: 0.06, MAPE: 935317922102.33%, R²: 1.00\n",
210 |       "\n",
211 |       "Testing Set Metrics:\n",
212 |       "MSE: 0.00, RMSE: 0.00, MAPE: 1490167536908.57%, R²: 1.00\n"
213 |      ]
214 |     }
215 |    ],
216 |    "source": [
217 |     "#3q)\n",
218 |     "\n",
219 |     "# Split the data into train and test\n",
220 |     "y = df.iloc[:,-1]\n",
221 |     "X = df.drop('Final_Marks',axis = 1)\n",
222 |     "X_train, X_test, y_train, y_test = train_test_splits(X, y)\n",
223 |     "\n",
224 |     "# Train the model\n",
225 |     "model = train_model(X_train, y_train)\n",
226 |     "y_train_pred = model.predict(X_train)\n",
227 |     "y_test_pred = model.predict(X_test)\n",
228 |     "\n",
229 |     "# for the training set\n",
230 |     "train_mse, train_rmse, train_mape, train_r2 = evaluate_model(y_train, y_train_pred)\n",
231 |     "\n",
232 |     "# for the testing set\n",
233 |     "test_mse, test_rmse, test_mape, test_r2 = evaluate_model(y_test, y_test_pred)\n",
234 |     "\n",
235 |     "print(\"Training Set Metrics:\")\n",
236 |     "print(f\"MSE: {train_mse:.2f}, RMSE: {train_rmse:.2f}, MAPE: {train_mape:.2%}, R²: {train_r2:.2f}\")\n",
237 |     "\n",
238 |     "print(\"\\nTesting Set Metrics:\")\n",
239 |     "print(f\"MSE: {test_mse:.2f}, RMSE: {test_rmse:.2f}, MAPE: {test_mape:.2%}, R²: {test_r2:.2f}\")\n"
240 |    ]
241 |   },
242 |   {
243 |    "cell_type": "code",
244 |    "execution_count": 26,
245 |    "id": "b6c53046-57da-4ddd-a06c-fe57e93cfce1",
246 |    "metadata": {},
247 |    "outputs": [
248 |     {
249 |      "name": "stderr",
250 |      "output_type": "stream",
251 |      "text": [
252 |       "C:\\Users\\mvenk\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
253 |       "  warnings.warn(\n",
254 |       "C:\\Users\\mvenk\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
255 |       "  warnings.warn(\n"
256 |      ]
257 |     },
258 |     {
259 |      "name": "stdout",
260 |      "output_type": "stream",
261 |      "text": [
262 |       "   Header_and_Main_Declaration  Incomprehensible_Code  \\\n",
263 |       "0                            2                    2.0   \n",
264 |       "1                            2                    2.0   \n",
265 |       "2                            2                    2.0   \n",
266 |       "3                            2                    1.0   \n",
267 |       "4                            2                    2.0   \n",
268 |       "\n",
269 |       "   Comprehensible_Code_with_logical_errors  \\\n",
270 |       "0                                        2   \n",
271 |       "1                                        2   \n",
272 |       "2                                        1   \n",
273 |       "3                                        1   \n",
274 |       "4                                        1   \n",
275 |       "\n",
276 |       "   Comprehensible_code_with_syntax_errors  Correct_code_and_output  \\\n",
277 |       "0                                     2.0                        2   \n",
278 |       "1                                     2.0                        0   \n",
279 |       "2                                     1.0                        1   \n",
280 |       "3                                     1.0                        0   \n",
281 |       "4                                     1.0                        0   \n",
282 |       "\n",
283 |       "   Final_Marks  Cluster  Target  \n",
284 |       "0           10        1       1  \n",
285 |       "1            8        1       1  \n",
286 |       "2            7        1       1  \n",
287 |       "3            5        0       1  \n",
288 |       "4            6        1       1  \n",
289 |       "Cluster Centers:\n",
290 |       " [[ 1.66796117e+00  1.74757282e-01  8.71844660e-01  9.69037892e-01\n",
291 |       "   2.27184466e-01 -1.11022302e-16  3.14563107e-01]\n",
292 |       " [ 1.84720121e+00  1.93065310e+00  1.19667171e+00  1.12111122e+00\n",
293 |       "   6.52042360e-01  1.00000000e+00  9.42511346e-01]]\n"
294 |      ]
295 |     }
296 |    ],
297 |    "source": [
298 |     "#4q)\n",
299 |     "from sklearn.cluster import KMeans\n",
300 |     "import pandas as pd\n",
301 |     "\n",
302 |     "# prepare data by removing the target variable\n",
303 |     "def prepare_data(df, target_column):\n",
304 |     "    X = df.drop(columns=[target_column])\n",
305 |     "    return X\n",
306 |     "\n",
307 |     "# perform K-means clustering\n",
308 |     "def perform_kmeans(X, n_clusters=3):\n",
309 |     "    kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n",
310 |     "    kmeans.fit(X)\n",
311 |     "    return kmeans\n",
312 |     "\n",
313 |     "def add_cluster_labels(df, kmeans):\n",
314 |     "    df['Cluster'] = kmeans.labels_\n",
315 |     "    return df\n",
316 |     "\n",
317 |     "def main(df, target_column, n_clusters=3):\n",
318 |     "    X = prepare_data(df, target_column)\n",
319 |     "    kmeans = perform_kmeans(X, n_clusters)\n",
320 |     "    df_with_clusters = add_cluster_labels(df, kmeans)\n",
321 |     "    return df_with_clusters, kmeans\n",
322 |     "\n",
323 |     "df_with_clusters, kmeans_model = main(df, target_column='Final_Marks', n_clusters=2)\n",
324 |     "\n",
325 |     "print(df_with_clusters.head())\n",
326 |     "\n",
327 |     "# Optionally, analyze the cluster centers\n",
328 |     "print(\"Cluster Centers:\\n\", kmeans_model.cluster_centers_)\n",
329 |     "\n"
330 |    ]
331 |   },
332 |   {
333 |    "cell_type": "code",
334 |    "execution_count": null,
335 |    "id": "e415b9b8-4797-4264-a857-56941476e413",
336 |    "metadata": {},
337 |    "outputs": [],
338 |    "source": [
339 |     "#5q)\n"
340 |    ]
341 |   }
342 |  ],
343 |  "metadata": {
344 |   "kernelspec": {
345 |    "display_name": "Python 3 (ipykernel)",
346 |    "language": "python",
347 |    "name": "python3"
348 |   },
349 |   "language_info": {
350 |    "codemirror_mode": {
351 |     "name": "ipython",
352 |     "version": 3
353 |    },
354 |    "file_extension": ".py",
355 |    "mimetype": "text/x-python",
356 |    "name": "python",
357 |    "nbconvert_exporter": "python",
358 |    "pygments_lexer": "ipython3",
359 |    "version": "3.11.7"
360 |   }
361 |  },
362 |  "nbformat": 4,
363 |  "nbformat_minor": 5
364 | }
365 | 


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/Lab-5/sss:
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1 | ss
2 | 


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/Lab-7/Lab-7.ipynb:
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   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 6,
   6 |    "id": "bee9a245-e15d-4b07-b4a2-6260659bb336",
   7 |    "metadata": {},
   8 |    "outputs": [
   9 |     {
  10 |      "name": "stderr",
  11 |      "output_type": "stream",
  12 |      "text": [
  13 |       "C:\\Users\\mvenk\\AppData\\Local\\Temp\\ipykernel_16564\\446592067.py:15: SettingWithCopyWarning: \n",
  14 |       "A value is trying to be set on a copy of a slice from a DataFrame\n",
  15 |       "\n",
  16 |       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
  17 |       "  X.fillna(0, inplace=True)\n"
  18 |      ]
  19 |     },
  20 |     {
  21 |      "name": "stdout",
  22 |      "output_type": "stream",
  23 |      "text": [
  24 |       "Best Perceptron Hyperparameters: {'tol': 1e-05, 'penalty': 'l2', 'max_iter': 1500, 'alpha': 4.641588833612782e-05}\n",
  25 |       "Best MLP Hyperparameters: {'solver': 'adam', 'max_iter': 300, 'learning_rate': 'constant', 'hidden_layer_sizes': (150, 100, 50), 'alpha': 4.641588833612782e-05, 'activation': 'tanh'}\n",
  26 |       "Perceptron Accuracy: 0.3342776203966006\n",
  27 |       "MLP Accuracy: 1.0\n"
  28 |      ]
  29 |     }
  30 |    ],
  31 |    "source": [
  32 |     "import pandas as pd\n",
  33 |     "import numpy as np\n",
  34 |     "\n",
  35 |     "data = pd.read_excel('ml-data.xlsx')\n",
  36 |     "\n",
  37 |     "# Extract the relevant columns (E to J)\n",
  38 |     "X = data[['Header_and_Main_Declaration', 'Incomprehensible_Code', \n",
  39 |     "          'Comprehensible_Code_with_logical_errors', \n",
  40 |     "          'Comprehensible_code_with_syntax_errors', \n",
  41 |     "          'Correct_code_and_output']]\n",
  42 |     "\n",
  43 |     "y = data['Final_Marks']\n",
  44 |     "\n",
  45 |     "# Handle any missing values if they exist\n",
  46 |     "X.fillna(0, inplace=True)\n",
  47 |     "\n",
  48 |     "# Split the data into training and testing sets\n",
  49 |     "from sklearn.model_selection import train_test_split\n",
  50 |     "\n",
  51 |     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
  52 |     "\n",
  53 |     "# Define the models: Perceptron and MLPClassifier\n",
  54 |     "from sklearn.linear_model import Perceptron\n",
  55 |     "from sklearn.neural_network import MLPClassifier\n",
  56 |     "from sklearn.model_selection import RandomizedSearchCV\n",
  57 |     "import numpy as np\n",
  58 |     "\n",
  59 |     "# Define Perceptron model and hyperparameters\n",
  60 |     "perceptron = Perceptron()\n",
  61 |     "param_dist_perceptron = {\n",
  62 |     "    'penalty': ['l2', 'l1', 'elasticnet'],\n",
  63 |     "    'alpha': np.logspace(-5, 1, 10),\n",
  64 |     "    'max_iter': [500, 1000, 1500, 2000],\n",
  65 |     "    'tol': [1e-3, 1e-4, 1e-5],\n",
  66 |     "}\n",
  67 |     "\n",
  68 |     "# Perform RandomizedSearchCV on Perceptron\n",
  69 |     "random_search_perceptron = RandomizedSearchCV(perceptron, param_distributions=param_dist_perceptron,\n",
  70 |     "                                              n_iter=10, cv=5, random_state=42, n_jobs=-1)\n",
  71 |     "random_search_perceptron.fit(X_train, y_train)\n",
  72 |     "\n",
  73 |     "# Best hyperparameters for Perceptron\n",
  74 |     "print(\"Best Perceptron Hyperparameters:\", random_search_perceptron.best_params_)\n",
  75 |     "\n",
  76 |     "# Define MLPClassifier model and hyperparameters\n",
  77 |     "mlp = MLPClassifier()\n",
  78 |     "param_dist_mlp = {\n",
  79 |     "    'hidden_layer_sizes': [(50,), (100,), (100, 50), (150, 100, 50)],\n",
  80 |     "    'activation': ['tanh', 'relu'],\n",
  81 |     "    'solver': ['adam', 'sgd'],\n",
  82 |     "    'alpha': np.logspace(-5, 1, 10),\n",
  83 |     "    'learning_rate': ['constant', 'adaptive'],\n",
  84 |     "    'max_iter': [200, 300, 500]\n",
  85 |     "}\n",
  86 |     "\n",
  87 |     "# Perform RandomizedSearchCV on MLPClassifier\n",
  88 |     "random_search_mlp = RandomizedSearchCV(mlp, param_distributions=param_dist_mlp, \n",
  89 |     "                                       n_iter=10, cv=5, random_state=42, n_jobs=-1)\n",
  90 |     "random_search_mlp.fit(X_train, y_train)\n",
  91 |     "\n",
  92 |     "# Best hyperparameters for MLP\n",
  93 |     "print(\"Best MLP Hyperparameters:\", random_search_mlp.best_params_)\n",
  94 |     "\n",
  95 |     "# Evaluate models on test set\n",
  96 |     "from sklearn.metrics import accuracy_score\n",
  97 |     "\n",
  98 |     "y_pred_perceptron = random_search_perceptron.predict(X_test)\n",
  99 |     "y_pred_mlp = random_search_mlp.predict(X_test)\n",
 100 |     "\n",
 101 |     "# Print accuracy\n",
 102 |     "print(\"Perceptron Accuracy:\", accuracy_score(y_test, y_pred_perceptron))\n",
 103 |     "print(\"MLP Accuracy:\", accuracy_score(y_test, y_pred_mlp))\n"
 104 |    ]
 105 |   },
 106 |   {
 107 |    "cell_type": "code",
 108 |    "execution_count": 9,
 109 |    "id": "12c0a923-054d-4ae9-9832-a0fc271d0017",
 110 |    "metadata": {},
 111 |    "outputs": [
 112 |     {
 113 |      "name": "stderr",
 114 |      "output_type": "stream",
 115 |      "text": [
 116 |       "C:\\Users\\mvenk\\AppData\\Local\\Temp\\ipykernel_16564\\3922117403.py:62: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
 117 |       "  results = pd.concat([results, temp_result], ignore_index=True)\n",
 118 |       "C:\\Users\\mvenk\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [15:34:40] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-06abd128ca6c1688d-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
 119 |       "Parameters: { \"use_label_encoder\" } are not used.\n",
 120 |       "\n",
 121 |       "  warnings.warn(smsg, UserWarning)\n"
 122 |      ]
 123 |     },
 124 |     {
 125 |      "data": {
 126 |       "text/html": [
 127 |        "<div>\n",
 128 |        "<style scoped>\n",
 129 |        "    .dataframe tbody tr th:only-of-type {\n",
 130 |        "        vertical-align: middle;\n",
 131 |        "    }\n",
 132 |        "\n",
 133 |        "    .dataframe tbody tr th {\n",
 134 |        "        vertical-align: top;\n",
 135 |        "    }\n",
 136 |        "\n",
 137 |        "    .dataframe thead th {\n",
 138 |        "        text-align: right;\n",
 139 |        "    }\n",
 140 |        "</style>\n",
 141 |        "<table border=\"1\" class=\"dataframe\">\n",
 142 |        "  <thead>\n",
 143 |        "    <tr style=\"text-align: right;\">\n",
 144 |        "      <th></th>\n",
 145 |        "      <th>Classifier</th>\n",
 146 |        "      <th>Accuracy</th>\n",
 147 |        "      <th>Precision</th>\n",
 148 |        "      <th>Recall</th>\n",
 149 |        "      <th>F1-Score</th>\n",
 150 |        "    </tr>\n",
 151 |        "  </thead>\n",
 152 |        "  <tbody>\n",
 153 |        "    <tr>\n",
 154 |        "      <th>0</th>\n",
 155 |        "      <td>Support Vector Machine</td>\n",
 156 |        "      <td>0.968839</td>\n",
 157 |        "      <td>0.964508</td>\n",
 158 |        "      <td>0.968839</td>\n",
 159 |        "      <td>0.966334</td>\n",
 160 |        "    </tr>\n",
 161 |        "    <tr>\n",
 162 |        "      <th>1</th>\n",
 163 |        "      <td>Decision Tree</td>\n",
 164 |        "      <td>0.929178</td>\n",
 165 |        "      <td>0.929824</td>\n",
 166 |        "      <td>0.929178</td>\n",
 167 |        "      <td>0.929137</td>\n",
 168 |        "    </tr>\n",
 169 |        "    <tr>\n",
 170 |        "      <th>2</th>\n",
 171 |        "      <td>Random Forest</td>\n",
 172 |        "      <td>0.929178</td>\n",
 173 |        "      <td>0.930304</td>\n",
 174 |        "      <td>0.929178</td>\n",
 175 |        "      <td>0.929217</td>\n",
 176 |        "    </tr>\n",
 177 |        "    <tr>\n",
 178 |        "      <th>3</th>\n",
 179 |        "      <td>AdaBoost</td>\n",
 180 |        "      <td>0.297450</td>\n",
 181 |        "      <td>0.289378</td>\n",
 182 |        "      <td>0.297450</td>\n",
 183 |        "      <td>0.230668</td>\n",
 184 |        "    </tr>\n",
 185 |        "    <tr>\n",
 186 |        "      <th>4</th>\n",
 187 |        "      <td>XGBoost</td>\n",
 188 |        "      <td>0.929178</td>\n",
 189 |        "      <td>0.929816</td>\n",
 190 |        "      <td>0.929178</td>\n",
 191 |        "      <td>0.929155</td>\n",
 192 |        "    </tr>\n",
 193 |        "    <tr>\n",
 194 |        "      <th>5</th>\n",
 195 |        "      <td>Naive Bayes</td>\n",
 196 |        "      <td>0.470255</td>\n",
 197 |        "      <td>0.410575</td>\n",
 198 |        "      <td>0.470255</td>\n",
 199 |        "      <td>0.429052</td>\n",
 200 |        "    </tr>\n",
 201 |        "  </tbody>\n",
 202 |        "</table>\n",
 203 |        "</div>"
 204 |       ],
 205 |       "text/plain": [
 206 |        "               Classifier  Accuracy  Precision    Recall  F1-Score\n",
 207 |        "0  Support Vector Machine  0.968839   0.964508  0.968839  0.966334\n",
 208 |        "1           Decision Tree  0.929178   0.929824  0.929178  0.929137\n",
 209 |        "2           Random Forest  0.929178   0.930304  0.929178  0.929217\n",
 210 |        "3                AdaBoost  0.297450   0.289378  0.297450  0.230668\n",
 211 |        "4                 XGBoost  0.929178   0.929816  0.929178  0.929155\n",
 212 |        "5             Naive Bayes  0.470255   0.410575  0.470255  0.429052"
 213 |       ]
 214 |      },
 215 |      "execution_count": 9,
 216 |      "metadata": {},
 217 |      "output_type": "execute_result"
 218 |     }
 219 |    ],
 220 |    "source": [
 221 |     "# Import necessary libraries\n",
 222 |     "from sklearn.svm import SVC\n",
 223 |     "from sklearn.tree import DecisionTreeClassifier\n",
 224 |     "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
 225 |     "from xgboost import XGBClassifier  # Ensure xgboost is installed\n",
 226 |     "from sklearn.naive_bayes import GaussianNB\n",
 227 |     "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
 228 |     "from sklearn.model_selection import train_test_split\n",
 229 |     "import pandas as pd\n",
 230 |     "\n",
 231 |     "# Extract features (E to I) and target (J column)\n",
 232 |     "X = data[['Header_and_Main_Declaration', 'Incomprehensible_Code', \n",
 233 |     "          'Comprehensible_Code_with_logical_errors', \n",
 234 |     "          'Comprehensible_code_with_syntax_errors', \n",
 235 |     "          'Correct_code_and_output']].copy()  # Use .copy() to avoid SettingWithCopyWarning\n",
 236 |     "\n",
 237 |     "y = data['Final_Marks']\n",
 238 |     "\n",
 239 |     "# Handle any missing values by reassigning X\n",
 240 |     "X = X.fillna(0)\n",
 241 |     "\n",
 242 |     "# Split the data into training and testing sets\n",
 243 |     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
 244 |     "\n",
 245 |     "# Define the classifiers (Removed CatBoostClassifier)\n",
 246 |     "classifiers = {\n",
 247 |     "    'Support Vector Machine': SVC(),\n",
 248 |     "    'Decision Tree': DecisionTreeClassifier(),\n",
 249 |     "    'Random Forest': RandomForestClassifier(),\n",
 250 |     "    'AdaBoost': AdaBoostClassifier(),\n",
 251 |     "    'XGBoost': XGBClassifier(use_label_encoder=False, eval_metric='mlogloss'),\n",
 252 |     "    'Naive Bayes': GaussianNB()\n",
 253 |     "}\n",
 254 |     "\n",
 255 |     "# Initialize a dataframe to store results\n",
 256 |     "results = pd.DataFrame(columns=['Classifier', 'Accuracy', 'Precision', 'Recall', 'F1-Score'])\n",
 257 |     "\n",
 258 |     "# Loop over classifiers, fit and predict, and store results\n",
 259 |     "for classifier_name, classifier in classifiers.items():\n",
 260 |     "    # Train the classifier\n",
 261 |     "    classifier.fit(X_train, y_train)\n",
 262 |     "    \n",
 263 |     "    # Predict on the test set\n",
 264 |     "    y_pred = classifier.predict(X_test)\n",
 265 |     "    \n",
 266 |     "    # Calculate performance metrics\n",
 267 |     "    accuracy = accuracy_score(y_test, y_pred)\n",
 268 |     "    precision = precision_score(y_test, y_pred, average='weighted', zero_division=0)\n",
 269 |     "    recall = recall_score(y_test, y_pred, average='weighted', zero_division=0)\n",
 270 |     "    f1 = f1_score(y_test, y_pred, average='weighted', zero_division=0)\n",
 271 |     "    \n",
 272 |     "    # Create a temporary DataFrame for this result\n",
 273 |     "    temp_result = pd.DataFrame({\n",
 274 |     "        'Classifier': [classifier_name],\n",
 275 |     "        'Accuracy': [accuracy],\n",
 276 |     "        'Precision': [precision],\n",
 277 |     "        'Recall': [recall],\n",
 278 |     "        'F1-Score': [f1]\n",
 279 |     "    })\n",
 280 |     "    \n",
 281 |     "    # Append the result to the results DataFrame using pd.concat\n",
 282 |     "    results = pd.concat([results, temp_result], ignore_index=True)\n",
 283 |     "\n",
 284 |     "# Display the results\n",
 285 |     "results\n"
 286 |    ]
 287 |   },
 288 |   {
 289 |    "cell_type": "code",
 290 |    "execution_count": 10,
 291 |    "id": "de50ad46-59f4-459c-9bb4-9d28c350af94",
 292 |    "metadata": {},
 293 |    "outputs": [
 294 |     {
 295 |      "name": "stderr",
 296 |      "output_type": "stream",
 297 |      "text": [
 298 |       "C:\\Users\\mvenk\\AppData\\Local\\Temp\\ipykernel_16564\\3418198608.py:71: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
 299 |       "  results = pd.concat([results, temp_result], ignore_index=True)\n",
 300 |       "C:\\Users\\mvenk\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [16:26:59] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-06abd128ca6c1688d-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
 301 |       "Parameters: { \"use_label_encoder\" } are not used.\n",
 302 |       "\n",
 303 |       "  warnings.warn(smsg, UserWarning)\n"
 304 |      ]
 305 |     },
 306 |     {
 307 |      "data": {
 308 |       "text/html": [
 309 |        "<div>\n",
 310 |        "<style scoped>\n",
 311 |        "    .dataframe tbody tr th:only-of-type {\n",
 312 |        "        vertical-align: middle;\n",
 313 |        "    }\n",
 314 |        "\n",
 315 |        "    .dataframe tbody tr th {\n",
 316 |        "        vertical-align: top;\n",
 317 |        "    }\n",
 318 |        "\n",
 319 |        "    .dataframe thead th {\n",
 320 |        "        text-align: right;\n",
 321 |        "    }\n",
 322 |        "</style>\n",
 323 |        "<table border=\"1\" class=\"dataframe\">\n",
 324 |        "  <thead>\n",
 325 |        "    <tr style=\"text-align: right;\">\n",
 326 |        "      <th></th>\n",
 327 |        "      <th>Classifier</th>\n",
 328 |        "      <th>Accuracy</th>\n",
 329 |        "      <th>Precision</th>\n",
 330 |        "      <th>Recall</th>\n",
 331 |        "      <th>F1-Score</th>\n",
 332 |        "    </tr>\n",
 333 |        "  </thead>\n",
 334 |        "  <tbody>\n",
 335 |        "    <tr>\n",
 336 |        "      <th>0</th>\n",
 337 |        "      <td>Support Vector Machine</td>\n",
 338 |        "      <td>0.968839</td>\n",
 339 |        "      <td>0.964508</td>\n",
 340 |        "      <td>0.968839</td>\n",
 341 |        "      <td>0.966334</td>\n",
 342 |        "    </tr>\n",
 343 |        "    <tr>\n",
 344 |        "      <th>1</th>\n",
 345 |        "      <td>Decision Tree</td>\n",
 346 |        "      <td>0.929178</td>\n",
 347 |        "      <td>0.929538</td>\n",
 348 |        "      <td>0.929178</td>\n",
 349 |        "      <td>0.929100</td>\n",
 350 |        "    </tr>\n",
 351 |        "    <tr>\n",
 352 |        "      <th>2</th>\n",
 353 |        "      <td>Random Forest</td>\n",
 354 |        "      <td>0.929178</td>\n",
 355 |        "      <td>0.930400</td>\n",
 356 |        "      <td>0.929178</td>\n",
 357 |        "      <td>0.929301</td>\n",
 358 |        "    </tr>\n",
 359 |        "    <tr>\n",
 360 |        "      <th>3</th>\n",
 361 |        "      <td>AdaBoost</td>\n",
 362 |        "      <td>0.297450</td>\n",
 363 |        "      <td>0.289378</td>\n",
 364 |        "      <td>0.297450</td>\n",
 365 |        "      <td>0.230668</td>\n",
 366 |        "    </tr>\n",
 367 |        "    <tr>\n",
 368 |        "      <th>4</th>\n",
 369 |        "      <td>XGBoost</td>\n",
 370 |        "      <td>0.929178</td>\n",
 371 |        "      <td>0.929816</td>\n",
 372 |        "      <td>0.929178</td>\n",
 373 |        "      <td>0.929155</td>\n",
 374 |        "    </tr>\n",
 375 |        "    <tr>\n",
 376 |        "      <th>5</th>\n",
 377 |        "      <td>Naive Bayes</td>\n",
 378 |        "      <td>0.470255</td>\n",
 379 |        "      <td>0.410575</td>\n",
 380 |        "      <td>0.470255</td>\n",
 381 |        "      <td>0.429052</td>\n",
 382 |        "    </tr>\n",
 383 |        "  </tbody>\n",
 384 |        "</table>\n",
 385 |        "</div>"
 386 |       ],
 387 |       "text/plain": [
 388 |        "               Classifier  Accuracy  Precision    Recall  F1-Score\n",
 389 |        "0  Support Vector Machine  0.968839   0.964508  0.968839  0.966334\n",
 390 |        "1           Decision Tree  0.929178   0.929538  0.929178  0.929100\n",
 391 |        "2           Random Forest  0.929178   0.930400  0.929178  0.929301\n",
 392 |        "3                AdaBoost  0.297450   0.289378  0.297450  0.230668\n",
 393 |        "4                 XGBoost  0.929178   0.929816  0.929178  0.929155\n",
 394 |        "5             Naive Bayes  0.470255   0.410575  0.470255  0.429052"
 395 |       ]
 396 |      },
 397 |      "execution_count": 10,
 398 |      "metadata": {},
 399 |      "output_type": "execute_result"
 400 |     }
 401 |    ],
 402 |    "source": [
 403 |     "# Import necessary libraries\n",
 404 |     "from sklearn.svm import SVC\n",
 405 |     "from sklearn.tree import DecisionTreeClassifier\n",
 406 |     "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
 407 |     "from xgboost import XGBClassifier  # Ensure xgboost is installed\n",
 408 |     "from sklearn.naive_bayes import GaussianNB\n",
 409 |     "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
 410 |     "from sklearn.model_selection import train_test_split\n",
 411 |     "import pandas as pd\n",
 412 |     "\n",
 413 |     "# Import CatBoost (optional, you can comment this if it's not installed)\n",
 414 |     "try:\n",
 415 |     "    from catboost import CatBoostClassifier\n",
 416 |     "except ImportError:\n",
 417 |     "    CatBoostClassifier = None\n",
 418 |     "\n",
 419 |     "# Extract features (E to I) and target (J column)\n",
 420 |     "X = data[['Header_and_Main_Declaration', 'Incomprehensible_Code', \n",
 421 |     "          'Comprehensible_Code_with_logical_errors', \n",
 422 |     "          'Comprehensible_code_with_syntax_errors', \n",
 423 |     "          'Correct_code_and_output']].copy()  # Use .copy() to avoid SettingWithCopyWarning\n",
 424 |     "\n",
 425 |     "y = data['Final_Marks']\n",
 426 |     "\n",
 427 |     "# Handle any missing values\n",
 428 |     "X = X.fillna(0)\n",
 429 |     "\n",
 430 |     "# Split the data into training and testing sets\n",
 431 |     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
 432 |     "\n",
 433 |     "# Define the classifiers (with CatBoost if available)\n",
 434 |     "classifiers = {\n",
 435 |     "    'Support Vector Machine': SVC(),\n",
 436 |     "    'Decision Tree': DecisionTreeClassifier(),\n",
 437 |     "    'Random Forest': RandomForestClassifier(),\n",
 438 |     "    'AdaBoost': AdaBoostClassifier(),\n",
 439 |     "    'XGBoost': XGBClassifier(use_label_encoder=False, eval_metric='mlogloss'),\n",
 440 |     "    'Naive Bayes': GaussianNB()\n",
 441 |     "}\n",
 442 |     "\n",
 443 |     "if CatBoostClassifier:\n",
 444 |     "    classifiers['CatBoost'] = CatBoostClassifier(silent=True)\n",
 445 |     "\n",
 446 |     "# Initialize a dataframe to store results\n",
 447 |     "results = pd.DataFrame(columns=['Classifier', 'Accuracy', 'Precision', 'Recall', 'F1-Score'])\n",
 448 |     "\n",
 449 |     "# Loop over classifiers, fit and predict, and store results\n",
 450 |     "for classifier_name, classifier in classifiers.items():\n",
 451 |     "    # Train the classifier\n",
 452 |     "    classifier.fit(X_train, y_train)\n",
 453 |     "    \n",
 454 |     "    # Predict on the test set\n",
 455 |     "    y_pred = classifier.predict(X_test)\n",
 456 |     "    \n",
 457 |     "    # Calculate performance metrics\n",
 458 |     "    accuracy = accuracy_score(y_test, y_pred)\n",
 459 |     "    precision = precision_score(y_test, y_pred, average='weighted', zero_division=0)\n",
 460 |     "    recall = recall_score(y_test, y_pred, average='weighted', zero_division=0)\n",
 461 |     "    f1 = f1_score(y_test, y_pred, average='weighted', zero_division=0)\n",
 462 |     "    \n",
 463 |     "    # Create a temporary DataFrame for this result\n",
 464 |     "    temp_result = pd.DataFrame({\n",
 465 |     "        'Classifier': [classifier_name],\n",
 466 |     "        'Accuracy': [accuracy],\n",
 467 |     "        'Precision': [precision],\n",
 468 |     "        'Recall': [recall],\n",
 469 |     "        'F1-Score': [f1]\n",
 470 |     "    })\n",
 471 |     "    \n",
 472 |     "    # Append the result to the results DataFrame using pd.concat\n",
 473 |     "    results = pd.concat([results, temp_result], ignore_index=True)\n",
 474 |     "\n",
 475 |     "# Display the results in a tabular form\n",
 476 |     "results\n"
 477 |    ]
 478 |   },
 479 |   {
 480 |    "cell_type": "code",
 481 |    "execution_count": 11,
 482 |    "id": "d454c021-2d21-4b6e-88a0-1a74f0411123",
 483 |    "metadata": {},
 484 |    "outputs": [
 485 |     {
 486 |      "name": "stdout",
 487 |      "output_type": "stream",
 488 |      "text": [
 489 |       "Collecting catboost\n",
 490 |       "  Downloading catboost-1.2.7-cp311-cp311-win_amd64.whl.metadata (1.2 kB)\n",
 491 |       "Collecting graphviz (from catboost)\n",
 492 |       "  Downloading graphviz-0.20.3-py3-none-any.whl.metadata (12 kB)\n",
 493 |       "Requirement already satisfied: matplotlib in c:\\users\\mvenk\\anaconda3\\lib\\site-packages (from catboost) (3.8.0)\n",
 494 |       "Requirement already satisfied: numpy<2.0,>=1.16.0 in c:\\users\\mvenk\\anaconda3\\lib\\site-packages (from catboost) (1.26.4)\n",
 495 |       "Requirement already satisfied: pandas>=0.24 in c:\\users\\mvenk\\anaconda3\\lib\\site-packages (from catboost) (2.1.4)\n",
 496 |       "Requirement already satisfied: scipy in c:\\users\\mvenk\\anaconda3\\lib\\site-packages (from catboost) (1.11.4)\n",
 497 |       "Requirement already satisfied: plotly in c:\\users\\mvenk\\anaconda3\\lib\\site-packages (from catboost) (5.9.0)\n",
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 499 |       "Collecting python-dateutil>=2.8.2 (from pandas>=0.24->catboost)\n",
 500 |       "  Downloading python_dateutil-2.9.0.post0-py2.py3-none-any.whl.metadata (8.4 kB)\n",
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 508 |       "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\mvenk\\anaconda3\\lib\\site-packages (from matplotlib->catboost) (10.2.0)\n",
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 510 |       "Requirement already satisfied: tenacity>=6.2.0 in c:\\users\\mvenk\\anaconda3\\lib\\site-packages (from plotly->catboost) (8.2.2)\n",
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1039 |       "Downloading graphviz-0.20.3-py3-none-any.whl (47 kB)\n",
1040 |       "   ---------------------------------------- 0.0/47.1 kB ? eta -:--:--\n",
1041 |       "   ---------------------------------------- 47.1/47.1 kB 2.5 MB/s eta 0:00:00\n",
1042 |       "Downloading python_dateutil-2.9.0.post0-py2.py3-none-any.whl (229 kB)\n",
1043 |       "   ---------------------------------------- 0.0/229.9 kB ? eta -:--:--\n",
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1046 |       "Installing collected packages: python-dateutil, graphviz, catboost\n",
1047 |       "  Attempting uninstall: python-dateutil\n",
1048 |       "    Found existing installation: python-dateutil 2.7.5\n",
1049 |       "    Uninstalling python-dateutil-2.7.5:\n",
1050 |       "      Successfully uninstalled python-dateutil-2.7.5\n",
1051 |       "Successfully installed catboost-1.2.7 graphviz-0.20.3 python-dateutil-2.9.0.post0\n"
1052 |      ]
1053 |     },
1054 |     {
1055 |      "name": "stderr",
1056 |      "output_type": "stream",
1057 |      "text": [
1058 |       "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
1059 |       "conda-repo-cli 1.0.75 requires requests_mock, which is not installed.\n",
1060 |       "conda-repo-cli 1.0.75 requires clyent==1.2.1, but you have clyent 1.2.2 which is incompatible.\n",
1061 |       "conda-repo-cli 1.0.75 requires python-dateutil==2.8.2, but you have python-dateutil 2.9.0.post0 which is incompatible.\n",
1062 |       "onelogin 3.1.6 requires python-dateutil~=2.7.0, but you have python-dateutil 2.9.0.post0 which is incompatible.\n",
1063 |       "onelogin 3.1.6 requires typing-extensions~=4.3.0, but you have typing-extensions 4.12.2 which is incompatible.\n"
1064 |      ]
1065 |     }
1066 |    ],
1067 |    "source": [
1068 |     "!pip install catboost\n"
1069 |    ]
1070 |   },
1071 |   {
1072 |    "cell_type": "code",
1073 |    "execution_count": 13,
1074 |    "id": "c2f47c1f-64b1-4332-9ce5-628e2a6766ac",
1075 |    "metadata": {},
1076 |    "outputs": [
1077 |     {
1078 |      "name": "stderr",
1079 |      "output_type": "stream",
1080 |      "text": [
1081 |       "C:\\Users\\mvenk\\AppData\\Local\\Temp\\ipykernel_16564\\1098097717.py:63: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
1082 |       "  results = pd.concat([results, temp_result], ignore_index=True)\n",
1083 |       "C:\\Users\\mvenk\\anaconda3\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [16:30:57] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-06abd128ca6c1688d-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
1084 |       "Parameters: { \"use_label_encoder\" } are not used.\n",
1085 |       "\n",
1086 |       "  warnings.warn(smsg, UserWarning)\n"
1087 |      ]
1088 |     },
1089 |     {
1090 |      "data": {
1091 |       "text/html": [
1092 |        "<div>\n",
1093 |        "<style scoped>\n",
1094 |        "    .dataframe tbody tr th:only-of-type {\n",
1095 |        "        vertical-align: middle;\n",
1096 |        "    }\n",
1097 |        "\n",
1098 |        "    .dataframe tbody tr th {\n",
1099 |        "        vertical-align: top;\n",
1100 |        "    }\n",
1101 |        "\n",
1102 |        "    .dataframe thead th {\n",
1103 |        "        text-align: right;\n",
1104 |        "    }\n",
1105 |        "</style>\n",
1106 |        "<table border=\"1\" class=\"dataframe\">\n",
1107 |        "  <thead>\n",
1108 |        "    <tr style=\"text-align: right;\">\n",
1109 |        "      <th></th>\n",
1110 |        "      <th>Classifier</th>\n",
1111 |        "      <th>Accuracy</th>\n",
1112 |        "      <th>Precision</th>\n",
1113 |        "      <th>Recall</th>\n",
1114 |        "      <th>F1-Score</th>\n",
1115 |        "    </tr>\n",
1116 |        "  </thead>\n",
1117 |        "  <tbody>\n",
1118 |        "    <tr>\n",
1119 |        "      <th>0</th>\n",
1120 |        "      <td>Support Vector Machine</td>\n",
1121 |        "      <td>0.968839</td>\n",
1122 |        "      <td>0.964508</td>\n",
1123 |        "      <td>0.968839</td>\n",
1124 |        "      <td>0.966334</td>\n",
1125 |        "    </tr>\n",
1126 |        "    <tr>\n",
1127 |        "      <th>1</th>\n",
1128 |        "      <td>Decision Tree</td>\n",
1129 |        "      <td>0.932011</td>\n",
1130 |        "      <td>0.932697</td>\n",
1131 |        "      <td>0.932011</td>\n",
1132 |        "      <td>0.932049</td>\n",
1133 |        "    </tr>\n",
1134 |        "    <tr>\n",
1135 |        "      <th>2</th>\n",
1136 |        "      <td>Random Forest</td>\n",
1137 |        "      <td>0.929178</td>\n",
1138 |        "      <td>0.930304</td>\n",
1139 |        "      <td>0.929178</td>\n",
1140 |        "      <td>0.929217</td>\n",
1141 |        "    </tr>\n",
1142 |        "    <tr>\n",
1143 |        "      <th>3</th>\n",
1144 |        "      <td>AdaBoost</td>\n",
1145 |        "      <td>0.297450</td>\n",
1146 |        "      <td>0.289378</td>\n",
1147 |        "      <td>0.297450</td>\n",
1148 |        "      <td>0.230668</td>\n",
1149 |        "    </tr>\n",
1150 |        "    <tr>\n",
1151 |        "      <th>4</th>\n",
1152 |        "      <td>XGBoost</td>\n",
1153 |        "      <td>0.929178</td>\n",
1154 |        "      <td>0.929816</td>\n",
1155 |        "      <td>0.929178</td>\n",
1156 |        "      <td>0.929155</td>\n",
1157 |        "    </tr>\n",
1158 |        "    <tr>\n",
1159 |        "      <th>5</th>\n",
1160 |        "      <td>CatBoost</td>\n",
1161 |        "      <td>0.934844</td>\n",
1162 |        "      <td>0.935726</td>\n",
1163 |        "      <td>0.934844</td>\n",
1164 |        "      <td>0.934917</td>\n",
1165 |        "    </tr>\n",
1166 |        "    <tr>\n",
1167 |        "      <th>6</th>\n",
1168 |        "      <td>Naive Bayes</td>\n",
1169 |        "      <td>0.470255</td>\n",
1170 |        "      <td>0.410575</td>\n",
1171 |        "      <td>0.470255</td>\n",
1172 |        "      <td>0.429052</td>\n",
1173 |        "    </tr>\n",
1174 |        "  </tbody>\n",
1175 |        "</table>\n",
1176 |        "</div>"
1177 |       ],
1178 |       "text/plain": [
1179 |        "               Classifier  Accuracy  Precision    Recall  F1-Score\n",
1180 |        "0  Support Vector Machine  0.968839   0.964508  0.968839  0.966334\n",
1181 |        "1           Decision Tree  0.932011   0.932697  0.932011  0.932049\n",
1182 |        "2           Random Forest  0.929178   0.930304  0.929178  0.929217\n",
1183 |        "3                AdaBoost  0.297450   0.289378  0.297450  0.230668\n",
1184 |        "4                 XGBoost  0.929178   0.929816  0.929178  0.929155\n",
1185 |        "5                CatBoost  0.934844   0.935726  0.934844  0.934917\n",
1186 |        "6             Naive Bayes  0.470255   0.410575  0.470255  0.429052"
1187 |       ]
1188 |      },
1189 |      "execution_count": 13,
1190 |      "metadata": {},
1191 |      "output_type": "execute_result"
1192 |     }
1193 |    ],
1194 |    "source": [
1195 |     "# Import necessary libraries\n",
1196 |     "from sklearn.svm import SVC\n",
1197 |     "from sklearn.tree import DecisionTreeClassifier\n",
1198 |     "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
1199 |     "from xgboost import XGBClassifier\n",
1200 |     "from catboost import CatBoostClassifier\n",
1201 |     "from sklearn.naive_bayes import GaussianNB\n",
1202 |     "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
1203 |     "from sklearn.model_selection import train_test_split\n",
1204 |     "import pandas as pd\n",
1205 |     "\n",
1206 |     "# Extract features (E to I) and target (J column)\n",
1207 |     "X = data[['Header_and_Main_Declaration', 'Incomprehensible_Code', \n",
1208 |     "          'Comprehensible_Code_with_logical_errors', \n",
1209 |     "          'Comprehensible_code_with_syntax_errors', \n",
1210 |     "          'Correct_code_and_output']]\n",
1211 |     "\n",
1212 |     "y = data['Final_Marks']\n",
1213 |     "\n",
1214 |     "# Handle any missing values by assigning the result to X\n",
1215 |     "X = X.fillna(0)\n",
1216 |     "\n",
1217 |     "# Split the data into training and testing sets\n",
1218 |     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
1219 |     "\n",
1220 |     "# Define the classifiers\n",
1221 |     "classifiers = {\n",
1222 |     "    'Support Vector Machine': SVC(),\n",
1223 |     "    'Decision Tree': DecisionTreeClassifier(),\n",
1224 |     "    'Random Forest': RandomForestClassifier(),\n",
1225 |     "    'AdaBoost': AdaBoostClassifier(),\n",
1226 |     "    'XGBoost': XGBClassifier(use_label_encoder=False, eval_metric='mlogloss'),\n",
1227 |     "    'CatBoost': CatBoostClassifier(silent=True),\n",
1228 |     "    'Naive Bayes': GaussianNB()\n",
1229 |     "}\n",
1230 |     "\n",
1231 |     "# Initialize a dataframe to store results\n",
1232 |     "results = pd.DataFrame(columns=['Classifier', 'Accuracy', 'Precision', 'Recall', 'F1-Score'])\n",
1233 |     "\n",
1234 |     "# Loop over classifiers, fit and predict, and store results\n",
1235 |     "for classifier_name, classifier in classifiers.items():\n",
1236 |     "    # Train the classifier\n",
1237 |     "    classifier.fit(X_train, y_train)\n",
1238 |     "    \n",
1239 |     "    # Predict on the test set\n",
1240 |     "    y_pred = classifier.predict(X_test)\n",
1241 |     "    \n",
1242 |     "    # Calculate performance metrics\n",
1243 |     "    accuracy = accuracy_score(y_test, y_pred)\n",
1244 |     "    precision = precision_score(y_test, y_pred, average='weighted', zero_division=0)\n",
1245 |     "    recall = recall_score(y_test, y_pred, average='weighted', zero_division=0)\n",
1246 |     "    f1 = f1_score(y_test, y_pred, average='weighted', zero_division=0)\n",
1247 |     "    \n",
1248 |     "    # Append the result using pd.concat\n",
1249 |     "    temp_result = pd.DataFrame({\n",
1250 |     "        'Classifier': [classifier_name],\n",
1251 |     "        'Accuracy': [accuracy],\n",
1252 |     "        'Precision': [precision],\n",
1253 |     "        'Recall': [recall],\n",
1254 |     "        'F1-Score': [f1]\n",
1255 |     "    })\n",
1256 |     "    \n",
1257 |     "    results = pd.concat([results, temp_result], ignore_index=True)\n",
1258 |     "\n",
1259 |     "# Display the results\n",
1260 |     "results\n"
1261 |    ]
1262 |   },
1263 |   {
1264 |    "cell_type": "code",
1265 |    "execution_count": null,
1266 |    "id": "e9c589a3-7a15-4476-aac8-66e9c414aa83",
1267 |    "metadata": {},
1268 |    "outputs": [],
1269 |    "source": []
1270 |   }
1271 |  ],
1272 |  "metadata": {
1273 |   "kernelspec": {
1274 |    "display_name": "Python 3 (ipykernel)",
1275 |    "language": "python",
1276 |    "name": "python3"
1277 |   },
1278 |   "language_info": {
1279 |    "codemirror_mode": {
1280 |     "name": "ipython",
1281 |     "version": 3
1282 |    },
1283 |    "file_extension": ".py",
1284 |    "mimetype": "text/x-python",
1285 |    "name": "python",
1286 |    "nbconvert_exporter": "python",
1287 |    "pygments_lexer": "ipython3",
1288 |    "version": "3.11.7"
1289 |   }
1290 |  },
1291 |  "nbformat": 4,
1292 |  "nbformat_minor": 5
1293 | }
1294 | 


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/Lab-7/ss:
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1 | s
2 | 


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/Lab6/ss:
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1 | s
2 | 


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/Lab_1 (1).ipynb:
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  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "provenance": []
  7 |     },
  8 |     "kernelspec": {
  9 |       "name": "python3",
 10 |       "display_name": "Python 3"
 11 |     },
 12 |     "language_info": {
 13 |       "name": "python"
 14 |     }
 15 |   },
 16 |   "cells": [
 17 |     {
 18 |       "cell_type": "code",
 19 |       "execution_count": 4,
 20 |       "metadata": {
 21 |         "colab": {
 22 |           "base_uri": "https://localhost:8080/"
 23 |         },
 24 |         "id": "iGgjnTmemMAx",
 25 |         "outputId": "7f16022b-9d02-44b3-b9f0-aae0119a14a4"
 26 |       },
 27 |       "outputs": [
 28 |         {
 29 |           "output_type": "stream",
 30 |           "name": "stdout",
 31 |           "text": [
 32 |             "Enter a String: hemesh\n",
 33 |             "Count of vowels in the given string: 2\n",
 34 |             "Count of consonants in the given string: 4\n"
 35 |           ]
 36 |         }
 37 |       ],
 38 |       "source": [
 39 |         "#1Q)\n",
 40 |         "def count_vowels(string):\n",
 41 |         "  vowels = 'AEIOUaeiou'\n",
 42 |         "  vowel_count = 0;\n",
 43 |         "  for char in string:\n",
 44 |         "    if char in vowels:\n",
 45 |         "      vowel_count = vowel_count + 1\n",
 46 |         "  return vowel_count\n",
 47 |         "\n",
 48 |         "def count_consonants(string):\n",
 49 |         "  vowels = 'AEIOUaeiou'\n",
 50 |         "  consonant_count = 0;\n",
 51 |         "  for char in string:\n",
 52 |         "    if char not in vowels:\n",
 53 |         "      consonant_count = consonant_count + 1\n",
 54 |         "  return consonant_count\n",
 55 |         "\n",
 56 |         "\n",
 57 |         "\n",
 58 |         "input_string = input('Enter a String: ')\n",
 59 |         "print('Count of vowels in the given string:',count_vowels(input_string))\n",
 60 |         "print('Count of consonants in the given string:',count_consonants(input_string))"
 61 |       ]
 62 |     },
 63 |     {
 64 |       "cell_type": "code",
 65 |       "source": [
 66 |         "#4Q)\n",
 67 |         "# this function is for taking matrix input\n",
 68 |         "def construct_matrix(rows,columns,matrix):\n",
 69 |         "  for i in range(rows):\n",
 70 |         "    matrix_row = []\n",
 71 |         "    for j in range(columns):\n",
 72 |         "      element = int(input(f'Enter element for row {i+1} and column {j+1}: '))\n",
 73 |         "      matrix_row.append(element)\n",
 74 |         "    matrix.append(matrix_row)\n",
 75 |         "#this function is for matrix transpose\n",
 76 |         "def transposes_matrix(matrix,rows,columns,transpose_matrix):\n",
 77 |         "  for i in range(columns):\n",
 78 |         "    transpose_matrix_row = []\n",
 79 |         "    for j in range(rows):\n",
 80 |         "      transpose_matrix_row.append(matrix[j][i])\n",
 81 |         "    transpose_matrix.append(transpose_matrix_row)\n",
 82 |         "\n",
 83 |         "#main program starts here\n",
 84 |         "#enter number of rows and colums\n",
 85 |         "rows = int(input('Enter number of rows: '))\n",
 86 |         "columns = int(input('Enter number of columns: '))\n",
 87 |         "\n",
 88 |         "matrix = []\n",
 89 |         "\n",
 90 |         "construct_matrix(rows,columns,matrix)\n",
 91 |         "print('Input Matrix: ')\n",
 92 |         "for i in range(rows):\n",
 93 |         "\n",
 94 |         "    for j in range(columns):\n",
 95 |         "        print(matrix[i][j], end = \" \")\n",
 96 |         "    print()\n",
 97 |         "\n",
 98 |         "\n",
 99 |         "transpose_matrix = []\n",
100 |         "\n",
101 |         "transposes_matrix(matrix,rows,columns,transpose_matrix)\n",
102 |         "print('Transpose Matrix: ')\n",
103 |         "for i in range(rows):\n",
104 |         "\n",
105 |         "    for j in range(columns):\n",
106 |         "        print(transpose_matrix[i][j], end = \" \")\n",
107 |         "    print()\n",
108 |         "\n"
109 |       ],
110 |       "metadata": {
111 |         "colab": {
112 |           "base_uri": "https://localhost:8080/"
113 |         },
114 |         "id": "YP9oSPVRnchR",
115 |         "outputId": "7c2d84f1-d656-4723-fddf-8e1798d80005"
116 |       },
117 |       "execution_count": 9,
118 |       "outputs": [
119 |         {
120 |           "output_type": "stream",
121 |           "name": "stdout",
122 |           "text": [
123 |             "Enter number of rows: 2\n",
124 |             "Enter number of columns: 2\n",
125 |             "Enter element for row 1 and column 1: 1\n",
126 |             "Enter element for row 1 and column 2: 2\n",
127 |             "Enter element for row 2 and column 1: 3\n",
128 |             "Enter element for row 2 and column 2: 4\n",
129 |             "Input Matrix: \n",
130 |             "1 2 \n",
131 |             "3 4 \n",
132 |             "Transpose Matrix: \n",
133 |             "1 3 \n",
134 |             "2 4 \n"
135 |           ]
136 |         }
137 |       ]
138 |     },
139 |     {
140 |       "cell_type": "code",
141 |       "source": [
142 |         "#2Q)\n",
143 |         "# this function is for taking matrix input\n",
144 |         "def construct_matrix(rows, columns):\n",
145 |         "    matrix = []\n",
146 |         "    for i in range(rows):\n",
147 |         "        matrix_row = []\n",
148 |         "        for j in range(columns):\n",
149 |         "            element = int(input(f'Enter element for row {i+1} and column {j+1}: '))\n",
150 |         "            matrix_row.append(element)\n",
151 |         "        matrix.append(matrix_row)\n",
152 |         "    return matrix\n",
153 |         "# this function is for taking matrix multiplication\n",
154 |         "def matrix_multiplication(matrix_a, matrix_b):\n",
155 |         "    rows_A = len(matrix_a)\n",
156 |         "    columns_A = len(matrix_a[0])\n",
157 |         "    rows_B = len(matrix_b)\n",
158 |         "    columns_B = len(matrix_b[0])\n",
159 |         "\n",
160 |         "    result_matrix = [[0] * columns_B for ele in range(rows_A)]\n",
161 |         "\n",
162 |         "    for i in range(rows_A):\n",
163 |         "        for j in range(columns_B):\n",
164 |         "            result_matrix[i][j] = sum(matrix_a[i][k] * matrix_b[k][j] for k in range(columns_A))\n",
165 |         "\n",
166 |         "    return result_matrix\n",
167 |         "\n",
168 |         "rows_A = int(input('Enter number of rows for matrix A: '))\n",
169 |         "columns_A = int(input('Enter number of columns for matrix A: '))\n",
170 |         "\n",
171 |         "rows_B = int(input('Enter number of rows for matrix B: '))\n",
172 |         "columns_B = int(input('Enter number of columns for matrix B: '))\n",
173 |         "\n",
174 |         "if columns_A != rows_B:\n",
175 |         "    print('Error: Multiplication is not possible')\n",
176 |         "    exit()\n",
177 |         "\n",
178 |         "print('Enter elements for Matrix A:')\n",
179 |         "matrix_a = construct_matrix(rows_A, columns_A)\n",
180 |         "\n",
181 |         "print('Enter elements for Matrix B:')\n",
182 |         "matrix_b = construct_matrix(rows_B, columns_B)\n",
183 |         "\n",
184 |         "print('Input Matrix A: ')\n",
185 |         "\n",
186 |         "for i in range(rows_A):\n",
187 |         "\n",
188 |         "    for j in range(columns_A):\n",
189 |         "        print(matrix_a[i][j], end = \" \")\n",
190 |         "    print()\n",
191 |         "\n",
192 |         "print('Input Matrix B: ')\n",
193 |         "\n",
194 |         "for i in range(rows_B):\n",
195 |         "\n",
196 |         "    for j in range(columns_B):\n",
197 |         "        print(matrix_b[i][j], end = \" \")\n",
198 |         "    print()\n",
199 |         "\n",
200 |         "result_matrix = matrix_multiplication(matrix_a, matrix_b)\n",
201 |         "\n",
202 |         "print('Result Matrix: ')\n",
203 |         "for i in range(rows_A):\n",
204 |         "\n",
205 |         "    for j in range(columns_B):\n",
206 |         "        print(result_matrix[i][j], end = \" \")\n",
207 |         "    print()\n"
208 |       ],
209 |       "metadata": {
210 |         "colab": {
211 |           "base_uri": "https://localhost:8080/"
212 |         },
213 |         "id": "cYIqnTcJsRb-",
214 |         "outputId": "f0aeee35-ea43-4923-9b0b-e28350376a66"
215 |       },
216 |       "execution_count": 17,
217 |       "outputs": [
218 |         {
219 |           "output_type": "stream",
220 |           "name": "stdout",
221 |           "text": [
222 |             "Enter number of rows for matrix A: 2\n",
223 |             "Enter number of columns for matrix A: 2\n",
224 |             "Enter number of rows for matrix B: 2\n",
225 |             "Enter number of columns for matrix B: 2\n",
226 |             "Enter elements for Matrix A:\n",
227 |             "Enter element for row 1 and column 1: 1\n",
228 |             "Enter element for row 1 and column 2: 2\n",
229 |             "Enter element for row 2 and column 1: 3\n",
230 |             "Enter element for row 2 and column 2: 4\n",
231 |             "Enter elements for Matrix B:\n",
232 |             "Enter element for row 1 and column 1: 5\n",
233 |             "Enter element for row 1 and column 2: 6\n",
234 |             "Enter element for row 2 and column 1: 7\n",
235 |             "Enter element for row 2 and column 2: 8\n",
236 |             "Input Matrix A: \n",
237 |             "1 2 \n",
238 |             "3 4 \n",
239 |             "Input Matrix B: \n",
240 |             "5 6 \n",
241 |             "7 8 \n",
242 |             "Result Matrix: \n",
243 |             "19 22 \n",
244 |             "43 50 \n"
245 |           ]
246 |         }
247 |       ]
248 |     },
249 |     {
250 |       "cell_type": "code",
251 |       "source": [
252 |         "#3Q)\n",
253 |         "# this function is for inputting list\n",
254 |         "def input_list():\n",
255 |         "    result = []\n",
256 |         "    i = 0\n",
257 |         "    while True:\n",
258 |         "        user_input = input(f'Enter element {i+1} (or type \"exit\" to finish): ')\n",
259 |         "        if user_input.lower() == \"exit\":\n",
260 |         "            break\n",
261 |         "        try:\n",
262 |         "            element = int(user_input)\n",
263 |         "            result.append(element)\n",
264 |         "            i += 1\n",
265 |         "        except ValueError:\n",
266 |         "            print(\"Please enter a valid integer or 'exit' to finish.\")\n",
267 |         "    return result\n",
268 |         "# this function is for getting count of commont elements in list\n",
269 |         "def common_count(list_a, list_b):\n",
270 |         "    count = 0\n",
271 |         "    for ele_a in list_a:\n",
272 |         "        if ele_a in list_b:\n",
273 |         "            count += 1\n",
274 |         "            list_b.remove(ele_a)\n",
275 |         "    return count\n",
276 |         "\n",
277 |         "print('Enter elements for the first list:')\n",
278 |         "list_a = input_list()\n",
279 |         "\n",
280 |         "print('Enter elements for the second list:')\n",
281 |         "list_b = input_list()\n",
282 |         "\n",
283 |         "common_ele_count = common_count(list_a, list_b)\n",
284 |         "print(f'Number of common elements: {common_ele_count}')\n"
285 |       ],
286 |       "metadata": {
287 |         "colab": {
288 |           "base_uri": "https://localhost:8080/"
289 |         },
290 |         "id": "-fhM4JsYwYA7",
291 |         "outputId": "9c750c39-8ef9-49fe-855c-318a4bf294ef"
292 |       },
293 |       "execution_count": 20,
294 |       "outputs": [
295 |         {
296 |           "output_type": "stream",
297 |           "name": "stdout",
298 |           "text": [
299 |             "Enter elements for the first list:\n",
300 |             "Enter element 1 (or type \"exit\" to finish): 1\n",
301 |             "Enter element 2 (or type \"exit\" to finish): 2\n",
302 |             "Enter element 3 (or type \"exit\" to finish): 3\n",
303 |             "Enter element 4 (or type \"exit\" to finish): 4\n",
304 |             "Enter element 5 (or type \"exit\" to finish): exit\n",
305 |             "Enter elements for the second list:\n",
306 |             "Enter element 1 (or type \"exit\" to finish): 5\n",
307 |             "Enter element 2 (or type \"exit\" to finish): 2\n",
308 |             "Enter element 3 (or type \"exit\" to finish): 3\n",
309 |             "Enter element 4 (or type \"exit\" to finish): 2\n",
310 |             "Enter element 5 (or type \"exit\" to finish): 4\n",
311 |             "Enter element 6 (or type \"exit\" to finish): \n",
312 |             "Please enter a valid integer or 'exit' to finish.\n",
313 |             "Enter element 6 (or type \"exit\" to finish): exit\n",
314 |             "Number of common elements: 3\n"
315 |           ]
316 |         }
317 |       ]
318 |     },
319 |     {
320 |       "cell_type": "code",
321 |       "source": [],
322 |       "metadata": {
323 |         "id": "iqqU15uf1QRz"
324 |       },
325 |       "execution_count": null,
326 |       "outputs": []
327 |     }
328 |   ]
329 | }


--------------------------------------------------------------------------------
/lab_2.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "provenance": []
  7 |     },
  8 |     "kernelspec": {
  9 |       "name": "python3",
 10 |       "display_name": "Python 3"
 11 |     },
 12 |     "language_info": {
 13 |       "name": "python"
 14 |     }
 15 |   },
 16 |   "cells": [
 17 |     {
 18 |       "cell_type": "code",
 19 |       "execution_count": 24,
 20 |       "metadata": {
 21 |         "colab": {
 22 |           "base_uri": "https://localhost:8080/"
 23 |         },
 24 |         "id": "uzfbpsfgPudm",
 25 |         "outputId": "c65f5337-21a7-4511-bca2-1892d49eed9e"
 26 |       },
 27 |       "outputs": [
 28 |         {
 29 |           "output_type": "stream",
 30 |           "name": "stdout",
 31 |           "text": [
 32 |             "Dimensionality of the vector space: 3\n",
 33 |             "Number of vectors in the vector space: 10\n",
 34 |             "Rank of Matrix A: 3\n",
 35 |             "\n",
 36 |             "Matrix A:\n",
 37 |             "[[20  6  2]\n",
 38 |             " [16  3  6]\n",
 39 |             " [27  6  2]\n",
 40 |             " [19  1  2]\n",
 41 |             " [24  4  2]\n",
 42 |             " [22  1  5]\n",
 43 |             " [15  4  2]\n",
 44 |             " [18  4  2]\n",
 45 |             " [21  1  4]\n",
 46 |             " [16  2  4]]\n",
 47 |             "\n",
 48 |             "Matrix C:\n",
 49 |             "[[386]\n",
 50 |             " [289]\n",
 51 |             " [393]\n",
 52 |             " [110]\n",
 53 |             " [280]\n",
 54 |             " [167]\n",
 55 |             " [271]\n",
 56 |             " [274]\n",
 57 |             " [148]\n",
 58 |             " [198]]\n",
 59 |             "\n",
 60 |             "Pseudo-Inverse of A:\n",
 61 |             "[[-0.01008596 -0.03124505  0.01013951  0.0290728   0.0182907   0.01161794\n",
 62 |             "  -0.00771348  0.00095458  0.01743623 -0.00542016]\n",
 63 |             " [ 0.09059668  0.07263726  0.03172933 -0.09071908 -0.01893196 -0.06926996\n",
 64 |             "   0.05675464  0.03152577 -0.07641966  0.00357352]\n",
 65 |             " [ 0.00299878  0.15874243 -0.05795468 -0.06609024 -0.06295043  0.03348017\n",
 66 |             "   0.01541831 -0.01070461  0.00029003  0.05938755]]\n",
 67 |             "\n",
 68 |             "Cost of each product available for sale (Matrix X):\n",
 69 |             "[[ 1.]\n",
 70 |             " [55.]\n",
 71 |             " [18.]]\n"
 72 |           ]
 73 |         }
 74 |       ],
 75 |       "source": [
 76 |         "import numpy as np\n",
 77 |         "import pandas as pd\n",
 78 |         "\n",
 79 |         "df = pd.read_excel('Lab Session Data.xlsx', sheet_name=0)\n",
 80 |         "\n",
 81 |         "A = df[['Candies (#)', 'Mangoes (Kg)', 'Milk Packets (#)']].to_numpy()\n",
 82 |         "C = df['Payment (Rs)'].to_numpy()\n",
 83 |         "\n",
 84 |         "C = C.reshape(-1, 1)\n",
 85 |         "\n",
 86 |         "dimensionality = A.shape[1]\n",
 87 |         "print(f\"Dimensionality of the vector space: {dimensionality}\")\n",
 88 |         "\n",
 89 |         "num_vectors = A.shape[0]\n",
 90 |         "print(f\"Number of vectors in the vector space: {num_vectors}\")\n",
 91 |         "\n",
 92 |         "rank_A = np.linalg.matrix_rank(A)\n",
 93 |         "print(f\"Rank of Matrix A: {rank_A}\")\n",
 94 |         "\n",
 95 |         "A_pseudo_inv = np.linalg.pinv(A)\n",
 96 |         "\n",
 97 |         "X = A_pseudo_inv @ C\n",
 98 |         "\n",
 99 |         "print(\"\\nMatrix A:\")\n",
100 |         "print(A)\n",
101 |         "\n",
102 |         "print(\"\\nMatrix C:\")\n",
103 |         "print(C)\n",
104 |         "\n",
105 |         "print(\"\\nPseudo-Inverse of A:\")\n",
106 |         "print(A_pseudo_inv)\n",
107 |         "\n",
108 |         "print(\"\\nCost of each product available for sale (Matrix X):\")\n",
109 |         "print(X)\n"
110 |       ]
111 |     },
112 |     {
113 |       "cell_type": "code",
114 |       "source": [],
115 |       "metadata": {
116 |         "id": "miX7qSvFQVS4"
117 |       },
118 |       "execution_count": null,
119 |       "outputs": []
120 |     }
121 |   ]
122 | }


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