├── LICENSE
├── Requirements.txt
├── README.md
├── Black Friday - Analysing Columns.ipynb
├── Black Friday - Analysis.ipynb
└── Black Friday -  Analysing Age & Marital Status.ipynb


/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2024 Abbireddy Venkata Chandu
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/Requirements.txt:
--------------------------------------------------------------------------------
 1 | Requirements for Black Friday Sales Analysis  
 2 | 
 3 | 1. Dataset:  
 4 |    - Sales transaction data, including customer demographics (age, gender, marital status), products purchased, and purchase amounts.  
 5 |    - Data fields like product categories, city tier, and occupation are essential for deeper insights.  
 6 | 
 7 | 2. Software and Tools:  
 8 |    - Python: Programming language for data analysis.  
 9 |    - Libraries:
10 |      - Pandas: Data cleaning and manipulation.  
11 |      - NumPy: Numerical computations.  
12 |      - Matplotlib/Seaborn: Data visualization.  
13 |      - Scikit-learn: For advanced analysis or predictive modeling (if needed).  
14 |      - Jupyter Notebook: For interactive analysis and visualization.  
15 | 
16 | 3. Environment Setup:  
17 |    - Python 3.x installed.  
18 |    - Virtual environment or dependency management tools like `pip` or `conda`.  
19 | 
20 | 4. Data Preprocessing :  
21 |    - Cleaning: Handle missing or inconsistent values.  
22 |    - Transformation: Convert categorical data to a usable format.  
23 |    - Aggregation: Combine or summarize data based on relevant features.  
24 | 
25 | 5. Skills :  
26 |    - Knowledge of data preprocessing and wrangling.  
27 |    - Experience with data visualization techniques.  
28 |    - Understanding of basic statistics for interpreting trends and insights.  
29 | 
30 | 6. Objectives :  
31 |    - Analyze trends by age, gender, marital status, and occupation.  
32 |    - Identify high-performing products and customer segments.  
33 |    - Derive actionable insights for strategic decision-making.  
34 | 
35 | This setup ensures a comprehensive and effective analysis of Black Friday sales data.
36 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
  1 | 
  2 | # Black Friday Sales Analysis
  3 | 
  4 | This project provides an in-depth analysis of Black Friday sales data. The analysis delves into key customer demographics, purchasing behaviors, and product trends to uncover insights that can guide strategic decision-making. By exploring multiple dimensions of the data, this project aims to enhance understanding of consumer preferences during Black Friday sales. 
  5 | 
  6 | ## Table of Contents
  7 | - [Overview](#overview)
  8 | - [Dataset](#dataset)
  9 | - [Analysis Scope](#analysis-scope)
 10 | - [Analysis Highlights](#analysis-highlights)
 11 | - [Technologies Used](#technologies-used)
 12 | - [How to Run](#how-to-run)
 13 | - [Results and Insights](#results-and-insights)
 14 | - [Contributing](#contributing)
 15 | - [License](#license)
 16 | 
 17 | ---
 18 | 
 19 | ## Overview
 20 | Black Friday sales represent a major retail event characterized by high-volume consumer activity. This analysis focuses on understanding the interplay of various demographic and behavioral factors that drive sales. By using data visualization and statistical methods, we aim to identify patterns and trends to answer key questions about customer purchasing behavior.
 21 | 
 22 | ---
 23 | 
 24 | ## Dataset
 25 | The dataset used in this project contains transactional data from a Black Friday sales event.  
 26 | Key attributes include:
 27 | - **Demographics**: Age, Gender, Marital Status
 28 | - **Behavioral**: Product ID, Purchase Amount
 29 | - **Occupational Data**: Occupation and City Tier  
 30 | 
 31 | ---
 32 | 
 33 | ## Analysis Scope
 34 | The analysis is divided into the following sections:  
 35 | 
 36 | ### 1. **Combining Age & Marital Status**
 37 |    - Examines how age groups correlate with marital status in determining purchasing power.
 38 |    - Identifies trends in spending behavior across single and married individuals.
 39 | 
 40 | ### 2. **Occupation and Products Analysis**
 41 |    - Analyzes purchasing patterns based on customers’ occupations.
 42 |    - Highlights which product categories are preferred by specific occupational groups.
 43 | 
 44 | ### 3. **Analyzing Age & Marital Status**
 45 |    - Provides deeper insights into how marital status affects spending within different age groups.
 46 |    - Identifies demographic segments with the highest contribution to sales.
 47 | 
 48 | ### 4. **Analyzing Gender**
 49 |    - Compares purchasing trends between male and female customers.
 50 |    - Evaluates the influence of gender on product preference and spending behavior.
 51 | 
 52 | ### 5. **Multi-Column Analysis**
 53 |    - Combines multiple dimensions, including age, occupation, and city tier, to gain holistic insights.
 54 |    - Visualizes relationships between demographic features and total sales.
 55 | 
 56 | ---
 57 | 
 58 | ## Technologies Used
 59 | - **Programming Language**: Python
 60 | - **Libraries**: 
 61 |   - Pandas (Data manipulation)
 62 |   - Matplotlib & Seaborn (Data visualization)
 63 |   - NumPy (Numerical computations)
 64 | - **Jupyter Notebook**: For interactive data analysis.
 65 | 
 66 | ---
 67 | 
 68 | ## How to Run
 69 | 1. Clone this repository:
 70 |    ```bash
 71 |    git clone https://github.com/venkat-0706/Black-Friday.git
 72 |    ```
 73 | 2. Navigate to the project directory:
 74 |    ```bash
 75 |    cd Black-Friday
 76 |    ```
 77 | 3. Install dependencies:
 78 |    ```bash
 79 |    pip install -r requirements.txt
 80 |    ```
 81 | 4. Run the Jupyter Notebook:
 82 |    ```bash
 83 |    jupyter notebook BlackFridayAnalys.ipynb
 84 |    ```
 85 | 
 86 | ---
 87 | 
 88 | ## Results and Insights
 89 | - **Age & Marital Status**: Married individuals in the 26-35 age group contribute the most to total sales.
 90 | - **Occupation Trends**: Certain occupations show a strong preference for high-value products.
 91 | - **Gender Analysis**: Males tend to spend more, but females show more diverse product preferences.
 92 | - **Multi-Dimensional Insights**: Customers from Tier 1 cities in the 26-45 age range dominate high-value purchases.
 93 | 
 94 | ---
 95 | 
 96 | ## Contributing
 97 | Contributions are welcome! If you'd like to enhance this project or add new analysis dimensions, please feel free to:
 98 | 1. Fork the repository.
 99 | 2. Create a new branch (`git checkout -b feature/YourFeature`).
100 | 3. Commit your changes (`git commit -m "Add your feature"`).
101 | 4. Push to the branch (`git push origin feature/YourFeature`).
102 | 5. Open a pull request.
103 | 
104 | ---
105 | 
106 | ## License
107 | This project is licensed under the [MIT License](LICENSE).
108 | 
109 | ---
110 | 
111 | ## Contact
112 | For any queries, feel free to reach out:
113 | - **Email**: chanduabbireddy247@gmail.com
114 | - **GitHub**: [venkat-0706](https://github.com/venkat-0706)
115 | - **Linkedin**: [chandu0706](https://www.linkedin.com/in/chandu0706/).
116 | ```
117 | 
118 | This template is modular, informative, and user-friendly, making it perfect for a GitHub repository. Adjust details like the repository URL and contact information as needed.
119 | 


--------------------------------------------------------------------------------
/Black Friday - Analysing Columns.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 2,
  6 |    "id": "b469ab75-f37b-49d0-8559-b821666ac192",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import pandas as pd\n"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 8,
 16 |    "id": "d475756c-dbd3-4623-bbf8-c975de3906f7",
 17 |    "metadata": {},
 18 |    "outputs": [],
 19 |    "source": [
 20 |     "df = pd.read_csv('BlackFriday.csv')\n",
 21 |     "\n",
 22 |     "del df['Product_Category_2']\n",
 23 |     "del df['Product_Category_3']"
 24 |    ]
 25 |   },
 26 |   {
 27 |    "cell_type": "code",
 28 |    "execution_count": 10,
 29 |    "id": "17f80875-38d1-4c23-a9f6-6d21602d05ad",
 30 |    "metadata": {},
 31 |    "outputs": [
 32 |     {
 33 |      "data": {
 34 |       "text/html": [
 35 |        "<div>\n",
 36 |        "<style scoped>\n",
 37 |        "    .dataframe tbody tr th:only-of-type {\n",
 38 |        "        vertical-align: middle;\n",
 39 |        "    }\n",
 40 |        "\n",
 41 |        "    .dataframe tbody tr th {\n",
 42 |        "        vertical-align: top;\n",
 43 |        "    }\n",
 44 |        "\n",
 45 |        "    .dataframe thead th {\n",
 46 |        "        text-align: right;\n",
 47 |        "    }\n",
 48 |        "</style>\n",
 49 |        "<table border=\"1\" class=\"dataframe\">\n",
 50 |        "  <thead>\n",
 51 |        "    <tr style=\"text-align: right;\">\n",
 52 |        "      <th></th>\n",
 53 |        "      <th>User_ID</th>\n",
 54 |        "      <th>Product_ID</th>\n",
 55 |        "      <th>Gender</th>\n",
 56 |        "      <th>Age</th>\n",
 57 |        "      <th>Occupation</th>\n",
 58 |        "      <th>City_Category</th>\n",
 59 |        "      <th>Stay_In_Current_City_Years</th>\n",
 60 |        "      <th>Marital_Status</th>\n",
 61 |        "      <th>Product_Category_1</th>\n",
 62 |        "      <th>Purchase</th>\n",
 63 |        "    </tr>\n",
 64 |        "  </thead>\n",
 65 |        "  <tbody>\n",
 66 |        "    <tr>\n",
 67 |        "      <th>0</th>\n",
 68 |        "      <td>1000001</td>\n",
 69 |        "      <td>P00069042</td>\n",
 70 |        "      <td>F</td>\n",
 71 |        "      <td>0-17</td>\n",
 72 |        "      <td>10</td>\n",
 73 |        "      <td>A</td>\n",
 74 |        "      <td>2</td>\n",
 75 |        "      <td>0</td>\n",
 76 |        "      <td>3</td>\n",
 77 |        "      <td>8370</td>\n",
 78 |        "    </tr>\n",
 79 |        "    <tr>\n",
 80 |        "      <th>1</th>\n",
 81 |        "      <td>1000001</td>\n",
 82 |        "      <td>P00248942</td>\n",
 83 |        "      <td>F</td>\n",
 84 |        "      <td>0-17</td>\n",
 85 |        "      <td>10</td>\n",
 86 |        "      <td>A</td>\n",
 87 |        "      <td>2</td>\n",
 88 |        "      <td>0</td>\n",
 89 |        "      <td>1</td>\n",
 90 |        "      <td>15200</td>\n",
 91 |        "    </tr>\n",
 92 |        "    <tr>\n",
 93 |        "      <th>2</th>\n",
 94 |        "      <td>1000001</td>\n",
 95 |        "      <td>P00087842</td>\n",
 96 |        "      <td>F</td>\n",
 97 |        "      <td>0-17</td>\n",
 98 |        "      <td>10</td>\n",
 99 |        "      <td>A</td>\n",
100 |        "      <td>2</td>\n",
101 |        "      <td>0</td>\n",
102 |        "      <td>12</td>\n",
103 |        "      <td>1422</td>\n",
104 |        "    </tr>\n",
105 |        "    <tr>\n",
106 |        "      <th>3</th>\n",
107 |        "      <td>1000001</td>\n",
108 |        "      <td>P00085442</td>\n",
109 |        "      <td>F</td>\n",
110 |        "      <td>0-17</td>\n",
111 |        "      <td>10</td>\n",
112 |        "      <td>A</td>\n",
113 |        "      <td>2</td>\n",
114 |        "      <td>0</td>\n",
115 |        "      <td>12</td>\n",
116 |        "      <td>1057</td>\n",
117 |        "    </tr>\n",
118 |        "    <tr>\n",
119 |        "      <th>4</th>\n",
120 |        "      <td>1000002</td>\n",
121 |        "      <td>P00285442</td>\n",
122 |        "      <td>M</td>\n",
123 |        "      <td>55+</td>\n",
124 |        "      <td>16</td>\n",
125 |        "      <td>C</td>\n",
126 |        "      <td>4+</td>\n",
127 |        "      <td>0</td>\n",
128 |        "      <td>8</td>\n",
129 |        "      <td>7969</td>\n",
130 |        "    </tr>\n",
131 |        "  </tbody>\n",
132 |        "</table>\n",
133 |        "</div>"
134 |       ],
135 |       "text/plain": [
136 |        "   User_ID Product_ID Gender   Age  Occupation City_Category  \\\n",
137 |        "0  1000001  P00069042      F  0-17          10             A   \n",
138 |        "1  1000001  P00248942      F  0-17          10             A   \n",
139 |        "2  1000001  P00087842      F  0-17          10             A   \n",
140 |        "3  1000001  P00085442      F  0-17          10             A   \n",
141 |        "4  1000002  P00285442      M   55+          16             C   \n",
142 |        "\n",
143 |        "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  Purchase  \n",
144 |        "0                          2               0                   3      8370  \n",
145 |        "1                          2               0                   1     15200  \n",
146 |        "2                          2               0                  12      1422  \n",
147 |        "3                          2               0                  12      1057  \n",
148 |        "4                         4+               0                   8      7969  "
149 |       ]
150 |      },
151 |      "execution_count": 10,
152 |      "metadata": {},
153 |      "output_type": "execute_result"
154 |     }
155 |    ],
156 |    "source": [
157 |     "df.head()"
158 |    ]
159 |   },
160 |   {
161 |    "cell_type": "code",
162 |    "execution_count": 14,
163 |    "id": "7d785535-59da-4455-bdbf-fc6ac42aabe1",
164 |    "metadata": {},
165 |    "outputs": [
166 |     {
167 |      "data": {
168 |       "text/plain": [
169 |        "5891"
170 |       ]
171 |      },
172 |      "execution_count": 14,
173 |      "metadata": {},
174 |      "output_type": "execute_result"
175 |     }
176 |    ],
177 |    "source": [
178 |     "df['User_ID'].nunique()"
179 |    ]
180 |   },
181 |   {
182 |    "cell_type": "code",
183 |    "execution_count": 16,
184 |    "id": "1a5ebcbe-9a28-46ae-875e-95ecb633f575",
185 |    "metadata": {},
186 |    "outputs": [
187 |     {
188 |      "data": {
189 |       "text/plain": [
190 |        "3623"
191 |       ]
192 |      },
193 |      "execution_count": 16,
194 |      "metadata": {},
195 |      "output_type": "execute_result"
196 |     }
197 |    ],
198 |    "source": [
199 |     "\n",
200 |     "df['Product_ID'].nunique()"
201 |    ]
202 |   },
203 |   {
204 |    "cell_type": "code",
205 |    "execution_count": 18,
206 |    "id": "338fcf0b-03ab-43d8-ae77-2bc7d191945d",
207 |    "metadata": {},
208 |    "outputs": [
209 |     {
210 |      "data": {
211 |       "text/plain": [
212 |        "array(['F', 'M'], dtype=object)"
213 |       ]
214 |      },
215 |      "execution_count": 18,
216 |      "metadata": {},
217 |      "output_type": "execute_result"
218 |     }
219 |    ],
220 |    "source": [
221 |     "\n",
222 |     "df['Gender'].unique()"
223 |    ]
224 |   },
225 |   {
226 |    "cell_type": "code",
227 |    "execution_count": 20,
228 |    "id": "42cef3cc-91d2-4b8d-8d86-0cc48943c5cb",
229 |    "metadata": {},
230 |    "outputs": [
231 |     {
232 |      "data": {
233 |       "text/plain": [
234 |        "array(['0-17', '55+', '26-35', '46-50', '51-55', '36-45', '18-25'],\n",
235 |        "      dtype=object)"
236 |       ]
237 |      },
238 |      "execution_count": 20,
239 |      "metadata": {},
240 |      "output_type": "execute_result"
241 |     }
242 |    ],
243 |    "source": [
244 |     "\n",
245 |     "df['Age'].unique()"
246 |    ]
247 |   },
248 |   {
249 |    "cell_type": "code",
250 |    "execution_count": 22,
251 |    "id": "c364a16b-857d-4683-ae66-b0befbe99672",
252 |    "metadata": {},
253 |    "outputs": [
254 |     {
255 |      "data": {
256 |       "text/plain": [
257 |        "array([10, 16, 15,  7, 20,  9,  1, 12, 17,  0,  3,  4, 11,  8, 19,  2, 18,\n",
258 |        "        5, 14, 13,  6], dtype=int64)"
259 |       ]
260 |      },
261 |      "execution_count": 22,
262 |      "metadata": {},
263 |      "output_type": "execute_result"
264 |     }
265 |    ],
266 |    "source": [
267 |     "\n",
268 |     "df['Occupation'].unique()\n"
269 |    ]
270 |   },
271 |   {
272 |    "cell_type": "code",
273 |    "execution_count": 24,
274 |    "id": "91fae47a-ab7b-4112-a87b-d160e778eecb",
275 |    "metadata": {},
276 |    "outputs": [
277 |     {
278 |      "data": {
279 |       "text/plain": [
280 |        "array(['A', 'C', 'B'], dtype=object)"
281 |       ]
282 |      },
283 |      "execution_count": 24,
284 |      "metadata": {},
285 |      "output_type": "execute_result"
286 |     }
287 |    ],
288 |    "source": [
289 |     "\n",
290 |     "df['City_Category'].unique()"
291 |    ]
292 |   },
293 |   {
294 |    "cell_type": "code",
295 |    "execution_count": 26,
296 |    "id": "e434dc19-136f-4b34-820c-7dae161c7b1f",
297 |    "metadata": {},
298 |    "outputs": [
299 |     {
300 |      "data": {
301 |       "text/plain": [
302 |        "array(['2', '4+', '3', '1', '0'], dtype=object)"
303 |       ]
304 |      },
305 |      "execution_count": 26,
306 |      "metadata": {},
307 |      "output_type": "execute_result"
308 |     }
309 |    ],
310 |    "source": [
311 |     "df['Stay_In_Current_City_Years'].unique()"
312 |    ]
313 |   },
314 |   {
315 |    "cell_type": "code",
316 |    "execution_count": 28,
317 |    "id": "da7aa06a-4fec-42ad-b498-ea9c35dd2542",
318 |    "metadata": {},
319 |    "outputs": [
320 |     {
321 |      "data": {
322 |       "text/plain": [
323 |        "array([0, 1], dtype=int64)"
324 |       ]
325 |      },
326 |      "execution_count": 28,
327 |      "metadata": {},
328 |      "output_type": "execute_result"
329 |     }
330 |    ],
331 |    "source": [
332 |     "\n",
333 |     "df['Marital_Status'].unique()"
334 |    ]
335 |   },
336 |   {
337 |    "cell_type": "code",
338 |    "execution_count": 30,
339 |    "id": "e88705c4-c9c2-4678-a700-050deace9743",
340 |    "metadata": {},
341 |    "outputs": [
342 |     {
343 |      "data": {
344 |       "text/plain": [
345 |        "array([ 3,  1, 12,  8,  5,  4,  2,  6, 14, 11, 13, 15,  7, 16, 18, 10, 17,\n",
346 |        "        9], dtype=int64)"
347 |       ]
348 |      },
349 |      "execution_count": 30,
350 |      "metadata": {},
351 |      "output_type": "execute_result"
352 |     }
353 |    ],
354 |    "source": [
355 |     "df['Product_Category_1'].unique()"
356 |    ]
357 |   },
358 |   {
359 |    "cell_type": "code",
360 |    "execution_count": 32,
361 |    "id": "883b1158-9f6d-4928-a3f7-4a548d2166ec",
362 |    "metadata": {},
363 |    "outputs": [
364 |     {
365 |      "data": {
366 |       "text/plain": [
367 |        "9333.859852635065"
368 |       ]
369 |      },
370 |      "execution_count": 32,
371 |      "metadata": {},
372 |      "output_type": "execute_result"
373 |     }
374 |    ],
375 |    "source": [
376 |     "\n",
377 |     "df['Purchase'].sum()/len(df['Purchase'])"
378 |    ]
379 |   },
380 |   {
381 |    "cell_type": "code",
382 |    "execution_count": 36,
383 |    "id": "71f281cb-79e8-4bd7-8151-6c81f074c1f7",
384 |    "metadata": {},
385 |    "outputs": [
386 |     {
387 |      "name": "stdout",
388 |      "output_type": "stream",
389 |      "text": [
390 |       "5891 \t: User_ID\n",
391 |       "3623 \t: Product_ID\n",
392 |       "2 \t: Gender\n",
393 |       "7 \t: Age\n",
394 |       "21 \t: Occupation\n",
395 |       "3 \t: City_Category\n",
396 |       "5 \t: Stay_In_Current_City_Years\n",
397 |       "2 \t: Marital_Status\n",
398 |       "18 \t: Product_Category_1\n",
399 |       "17959 \t: Purchase\n"
400 |      ]
401 |     }
402 |    ],
403 |    "source": [
404 |     "\n",
405 |     "for column in df.columns:\n",
406 |     "    print(df[column].nunique() , \"\\t:\", column)"
407 |    ]
408 |   },
409 |   {
410 |    "cell_type": "code",
411 |    "execution_count": null,
412 |    "id": "825f4eca-d4e9-4e1f-bdf4-59493d2e27fb",
413 |    "metadata": {},
414 |    "outputs": [],
415 |    "source": []
416 |   }
417 |  ],
418 |  "metadata": {
419 |   "kernelspec": {
420 |    "display_name": "Python 3 (ipykernel)",
421 |    "language": "python",
422 |    "name": "python3"
423 |   },
424 |   "language_info": {
425 |    "codemirror_mode": {
426 |     "name": "ipython",
427 |     "version": 3
428 |    },
429 |    "file_extension": ".py",
430 |    "mimetype": "text/x-python",
431 |    "name": "python",
432 |    "nbconvert_exporter": "python",
433 |    "pygments_lexer": "ipython3",
434 |    "version": "3.12.4"
435 |   }
436 |  },
437 |  "nbformat": 4,
438 |  "nbformat_minor": 5
439 | }
440 | 


--------------------------------------------------------------------------------
/Black Friday - Analysis.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 2,
   6 |    "id": "bb3dd480-061f-46c6-a281-d4b873a37131",
   7 |    "metadata": {},
   8 |    "outputs": [],
   9 |    "source": [
  10 |     "import pandas as pd\n"
  11 |    ]
  12 |   },
  13 |   {
  14 |    "cell_type": "code",
  15 |    "execution_count": 4,
  16 |    "id": "ae07eba1-285e-4f56-aa55-33e29eaa8a58",
  17 |    "metadata": {},
  18 |    "outputs": [],
  19 |    "source": [
  20 |     "df = pd.read_csv('BlackFriday.csv')\n"
  21 |    ]
  22 |   },
  23 |   {
  24 |    "cell_type": "code",
  25 |    "execution_count": 6,
  26 |    "id": "c364e5e9-1716-4668-bd75-4676e1d46f8a",
  27 |    "metadata": {},
  28 |    "outputs": [
  29 |     {
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  47 |        "  <thead>\n",
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  49 |        "      <th></th>\n",
  50 |        "      <th>User_ID</th>\n",
  51 |        "      <th>Product_ID</th>\n",
  52 |        "      <th>Gender</th>\n",
  53 |        "      <th>Age</th>\n",
  54 |        "      <th>Occupation</th>\n",
  55 |        "      <th>City_Category</th>\n",
  56 |        "      <th>Stay_In_Current_City_Years</th>\n",
  57 |        "      <th>Marital_Status</th>\n",
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  59 |        "      <th>Product_Category_2</th>\n",
  60 |        "      <th>Product_Category_3</th>\n",
  61 |        "      <th>Purchase</th>\n",
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  97 |        "      <td>1000001</td>\n",
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 197 |        "      <td>NaN</td>\n",
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 208 |        "      <td>1</td>\n",
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 211 |        "      <td>NaN</td>\n",
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 227 |        "      <td>NaN</td>\n",
 228 |        "      <td>6875</td>\n",
 229 |        "    </tr>\n",
 230 |        "  </tbody>\n",
 231 |        "</table>\n",
 232 |        "<p>537577 rows × 12 columns</p>\n",
 233 |        "</div>"
 234 |       ],
 235 |       "text/plain": [
 236 |        "        User_ID Product_ID Gender    Age  Occupation City_Category  \\\n",
 237 |        "0       1000001  P00069042      F   0-17          10             A   \n",
 238 |        "1       1000001  P00248942      F   0-17          10             A   \n",
 239 |        "2       1000001  P00087842      F   0-17          10             A   \n",
 240 |        "3       1000001  P00085442      F   0-17          10             A   \n",
 241 |        "4       1000002  P00285442      M    55+          16             C   \n",
 242 |        "...         ...        ...    ...    ...         ...           ...   \n",
 243 |        "537572  1004737  P00193542      M  36-45          16             C   \n",
 244 |        "537573  1004737  P00111142      M  36-45          16             C   \n",
 245 |        "537574  1004737  P00345942      M  36-45          16             C   \n",
 246 |        "537575  1004737  P00285842      M  36-45          16             C   \n",
 247 |        "537576  1004737  P00118242      M  36-45          16             C   \n",
 248 |        "\n",
 249 |        "       Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
 250 |        "0                               2               0                   3   \n",
 251 |        "1                               2               0                   1   \n",
 252 |        "2                               2               0                  12   \n",
 253 |        "3                               2               0                  12   \n",
 254 |        "4                              4+               0                   8   \n",
 255 |        "...                           ...             ...                 ...   \n",
 256 |        "537572                          1               0                   1   \n",
 257 |        "537573                          1               0                   1   \n",
 258 |        "537574                          1               0                   8   \n",
 259 |        "537575                          1               0                   5   \n",
 260 |        "537576                          1               0                   5   \n",
 261 |        "\n",
 262 |        "        Product_Category_2  Product_Category_3  Purchase  \n",
 263 |        "0                      NaN                 NaN      8370  \n",
 264 |        "1                      6.0                14.0     15200  \n",
 265 |        "2                      NaN                 NaN      1422  \n",
 266 |        "3                     14.0                 NaN      1057  \n",
 267 |        "4                      NaN                 NaN      7969  \n",
 268 |        "...                    ...                 ...       ...  \n",
 269 |        "537572                 2.0                 NaN     11664  \n",
 270 |        "537573                15.0                16.0     19196  \n",
 271 |        "537574                15.0                 NaN      8043  \n",
 272 |        "537575                 NaN                 NaN      7172  \n",
 273 |        "537576                 8.0                 NaN      6875  \n",
 274 |        "\n",
 275 |        "[537577 rows x 12 columns]"
 276 |       ]
 277 |      },
 278 |      "execution_count": 6,
 279 |      "metadata": {},
 280 |      "output_type": "execute_result"
 281 |     }
 282 |    ],
 283 |    "source": [
 284 |     "df\n"
 285 |    ]
 286 |   },
 287 |   {
 288 |    "cell_type": "code",
 289 |    "execution_count": 8,
 290 |    "id": "253a6c80-3d5e-4358-9238-afd1e9864589",
 291 |    "metadata": {},
 292 |    "outputs": [
 293 |     {
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 371 |        "      <td>NaN</td>\n",
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 374 |        "    <tr>\n",
 375 |        "      <th>3</th>\n",
 376 |        "      <td>1000001</td>\n",
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 386 |        "      <td>NaN</td>\n",
 387 |        "      <td>1057</td>\n",
 388 |        "    </tr>\n",
 389 |        "    <tr>\n",
 390 |        "      <th>4</th>\n",
 391 |        "      <td>1000002</td>\n",
 392 |        "      <td>P00285442</td>\n",
 393 |        "      <td>M</td>\n",
 394 |        "      <td>55+</td>\n",
 395 |        "      <td>16</td>\n",
 396 |        "      <td>C</td>\n",
 397 |        "      <td>4+</td>\n",
 398 |        "      <td>0</td>\n",
 399 |        "      <td>8</td>\n",
 400 |        "      <td>NaN</td>\n",
 401 |        "      <td>NaN</td>\n",
 402 |        "      <td>7969</td>\n",
 403 |        "    </tr>\n",
 404 |        "  </tbody>\n",
 405 |        "</table>\n",
 406 |        "</div>"
 407 |       ],
 408 |       "text/plain": [
 409 |        "   User_ID Product_ID Gender   Age  Occupation City_Category  \\\n",
 410 |        "0  1000001  P00069042      F  0-17          10             A   \n",
 411 |        "1  1000001  P00248942      F  0-17          10             A   \n",
 412 |        "2  1000001  P00087842      F  0-17          10             A   \n",
 413 |        "3  1000001  P00085442      F  0-17          10             A   \n",
 414 |        "4  1000002  P00285442      M   55+          16             C   \n",
 415 |        "\n",
 416 |        "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
 417 |        "0                          2               0                   3   \n",
 418 |        "1                          2               0                   1   \n",
 419 |        "2                          2               0                  12   \n",
 420 |        "3                          2               0                  12   \n",
 421 |        "4                         4+               0                   8   \n",
 422 |        "\n",
 423 |        "   Product_Category_2  Product_Category_3  Purchase  \n",
 424 |        "0                 NaN                 NaN      8370  \n",
 425 |        "1                 6.0                14.0     15200  \n",
 426 |        "2                 NaN                 NaN      1422  \n",
 427 |        "3                14.0                 NaN      1057  \n",
 428 |        "4                 NaN                 NaN      7969  "
 429 |       ]
 430 |      },
 431 |      "execution_count": 8,
 432 |      "metadata": {},
 433 |      "output_type": "execute_result"
 434 |     }
 435 |    ],
 436 |    "source": [
 437 |     "df.head()"
 438 |    ]
 439 |   },
 440 |   {
 441 |    "cell_type": "code",
 442 |    "execution_count": 10,
 443 |    "id": "dd1007cd-51e6-431e-bb63-1fbcadf8c264",
 444 |    "metadata": {},
 445 |    "outputs": [
 446 |     {
 447 |      "name": "stdout",
 448 |      "output_type": "stream",
 449 |      "text": [
 450 |       "<class 'pandas.core.frame.DataFrame'>\n",
 451 |       "RangeIndex: 537577 entries, 0 to 537576\n",
 452 |       "Data columns (total 12 columns):\n",
 453 |       " #   Column                      Non-Null Count   Dtype  \n",
 454 |       "---  ------                      --------------   -----  \n",
 455 |       " 0   User_ID                     537577 non-null  int64  \n",
 456 |       " 1   Product_ID                  537577 non-null  object \n",
 457 |       " 2   Gender                      537577 non-null  object \n",
 458 |       " 3   Age                         537577 non-null  object \n",
 459 |       " 4   Occupation                  537577 non-null  int64  \n",
 460 |       " 5   City_Category               537577 non-null  object \n",
 461 |       " 6   Stay_In_Current_City_Years  537577 non-null  object \n",
 462 |       " 7   Marital_Status              537577 non-null  int64  \n",
 463 |       " 8   Product_Category_1          537577 non-null  int64  \n",
 464 |       " 9   Product_Category_2          370591 non-null  float64\n",
 465 |       " 10  Product_Category_3          164278 non-null  float64\n",
 466 |       " 11  Purchase                    537577 non-null  int64  \n",
 467 |       "dtypes: float64(2), int64(5), object(5)\n",
 468 |       "memory usage: 49.2+ MB\n"
 469 |      ]
 470 |     }
 471 |    ],
 472 |    "source": [
 473 |     "df.info()"
 474 |    ]
 475 |   },
 476 |   {
 477 |    "cell_type": "code",
 478 |    "execution_count": 12,
 479 |    "id": "7241fd1d-969f-4b71-9f1a-a8bd62da6446",
 480 |    "metadata": {},
 481 |    "outputs": [
 482 |     {
 483 |      "data": {
 484 |       "text/html": [
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 498 |        "</style>\n",
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 502 |        "      <th></th>\n",
 503 |        "      <th>User_ID</th>\n",
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 525 |        "      <td>False</td>\n",
 526 |        "      <td>False</td>\n",
 527 |        "      <td>False</td>\n",
 528 |        "      <td>False</td>\n",
 529 |        "      <td>True</td>\n",
 530 |        "      <td>True</td>\n",
 531 |        "      <td>False</td>\n",
 532 |        "    </tr>\n",
 533 |        "    <tr>\n",
 534 |        "      <th>1</th>\n",
 535 |        "      <td>False</td>\n",
 536 |        "      <td>False</td>\n",
 537 |        "      <td>False</td>\n",
 538 |        "      <td>False</td>\n",
 539 |        "      <td>False</td>\n",
 540 |        "      <td>False</td>\n",
 541 |        "      <td>False</td>\n",
 542 |        "      <td>False</td>\n",
 543 |        "      <td>False</td>\n",
 544 |        "      <td>False</td>\n",
 545 |        "      <td>False</td>\n",
 546 |        "      <td>False</td>\n",
 547 |        "    </tr>\n",
 548 |        "    <tr>\n",
 549 |        "      <th>2</th>\n",
 550 |        "      <td>False</td>\n",
 551 |        "      <td>False</td>\n",
 552 |        "      <td>False</td>\n",
 553 |        "      <td>False</td>\n",
 554 |        "      <td>False</td>\n",
 555 |        "      <td>False</td>\n",
 556 |        "      <td>False</td>\n",
 557 |        "      <td>False</td>\n",
 558 |        "      <td>False</td>\n",
 559 |        "      <td>True</td>\n",
 560 |        "      <td>True</td>\n",
 561 |        "      <td>False</td>\n",
 562 |        "    </tr>\n",
 563 |        "    <tr>\n",
 564 |        "      <th>3</th>\n",
 565 |        "      <td>False</td>\n",
 566 |        "      <td>False</td>\n",
 567 |        "      <td>False</td>\n",
 568 |        "      <td>False</td>\n",
 569 |        "      <td>False</td>\n",
 570 |        "      <td>False</td>\n",
 571 |        "      <td>False</td>\n",
 572 |        "      <td>False</td>\n",
 573 |        "      <td>False</td>\n",
 574 |        "      <td>False</td>\n",
 575 |        "      <td>True</td>\n",
 576 |        "      <td>False</td>\n",
 577 |        "    </tr>\n",
 578 |        "    <tr>\n",
 579 |        "      <th>4</th>\n",
 580 |        "      <td>False</td>\n",
 581 |        "      <td>False</td>\n",
 582 |        "      <td>False</td>\n",
 583 |        "      <td>False</td>\n",
 584 |        "      <td>False</td>\n",
 585 |        "      <td>False</td>\n",
 586 |        "      <td>False</td>\n",
 587 |        "      <td>False</td>\n",
 588 |        "      <td>False</td>\n",
 589 |        "      <td>True</td>\n",
 590 |        "      <td>True</td>\n",
 591 |        "      <td>False</td>\n",
 592 |        "    </tr>\n",
 593 |        "    <tr>\n",
 594 |        "      <th>...</th>\n",
 595 |        "      <td>...</td>\n",
 596 |        "      <td>...</td>\n",
 597 |        "      <td>...</td>\n",
 598 |        "      <td>...</td>\n",
 599 |        "      <td>...</td>\n",
 600 |        "      <td>...</td>\n",
 601 |        "      <td>...</td>\n",
 602 |        "      <td>...</td>\n",
 603 |        "      <td>...</td>\n",
 604 |        "      <td>...</td>\n",
 605 |        "      <td>...</td>\n",
 606 |        "      <td>...</td>\n",
 607 |        "    </tr>\n",
 608 |        "    <tr>\n",
 609 |        "      <th>537572</th>\n",
 610 |        "      <td>False</td>\n",
 611 |        "      <td>False</td>\n",
 612 |        "      <td>False</td>\n",
 613 |        "      <td>False</td>\n",
 614 |        "      <td>False</td>\n",
 615 |        "      <td>False</td>\n",
 616 |        "      <td>False</td>\n",
 617 |        "      <td>False</td>\n",
 618 |        "      <td>False</td>\n",
 619 |        "      <td>False</td>\n",
 620 |        "      <td>True</td>\n",
 621 |        "      <td>False</td>\n",
 622 |        "    </tr>\n",
 623 |        "    <tr>\n",
 624 |        "      <th>537573</th>\n",
 625 |        "      <td>False</td>\n",
 626 |        "      <td>False</td>\n",
 627 |        "      <td>False</td>\n",
 628 |        "      <td>False</td>\n",
 629 |        "      <td>False</td>\n",
 630 |        "      <td>False</td>\n",
 631 |        "      <td>False</td>\n",
 632 |        "      <td>False</td>\n",
 633 |        "      <td>False</td>\n",
 634 |        "      <td>False</td>\n",
 635 |        "      <td>False</td>\n",
 636 |        "      <td>False</td>\n",
 637 |        "    </tr>\n",
 638 |        "    <tr>\n",
 639 |        "      <th>537574</th>\n",
 640 |        "      <td>False</td>\n",
 641 |        "      <td>False</td>\n",
 642 |        "      <td>False</td>\n",
 643 |        "      <td>False</td>\n",
 644 |        "      <td>False</td>\n",
 645 |        "      <td>False</td>\n",
 646 |        "      <td>False</td>\n",
 647 |        "      <td>False</td>\n",
 648 |        "      <td>False</td>\n",
 649 |        "      <td>False</td>\n",
 650 |        "      <td>True</td>\n",
 651 |        "      <td>False</td>\n",
 652 |        "    </tr>\n",
 653 |        "    <tr>\n",
 654 |        "      <th>537575</th>\n",
 655 |        "      <td>False</td>\n",
 656 |        "      <td>False</td>\n",
 657 |        "      <td>False</td>\n",
 658 |        "      <td>False</td>\n",
 659 |        "      <td>False</td>\n",
 660 |        "      <td>False</td>\n",
 661 |        "      <td>False</td>\n",
 662 |        "      <td>False</td>\n",
 663 |        "      <td>False</td>\n",
 664 |        "      <td>True</td>\n",
 665 |        "      <td>True</td>\n",
 666 |        "      <td>False</td>\n",
 667 |        "    </tr>\n",
 668 |        "    <tr>\n",
 669 |        "      <th>537576</th>\n",
 670 |        "      <td>False</td>\n",
 671 |        "      <td>False</td>\n",
 672 |        "      <td>False</td>\n",
 673 |        "      <td>False</td>\n",
 674 |        "      <td>False</td>\n",
 675 |        "      <td>False</td>\n",
 676 |        "      <td>False</td>\n",
 677 |        "      <td>False</td>\n",
 678 |        "      <td>False</td>\n",
 679 |        "      <td>False</td>\n",
 680 |        "      <td>True</td>\n",
 681 |        "      <td>False</td>\n",
 682 |        "    </tr>\n",
 683 |        "  </tbody>\n",
 684 |        "</table>\n",
 685 |        "<p>537577 rows × 12 columns</p>\n",
 686 |        "</div>"
 687 |       ],
 688 |       "text/plain": [
 689 |        "        User_ID  Product_ID  Gender    Age  Occupation  City_Category  \\\n",
 690 |        "0         False       False   False  False       False          False   \n",
 691 |        "1         False       False   False  False       False          False   \n",
 692 |        "2         False       False   False  False       False          False   \n",
 693 |        "3         False       False   False  False       False          False   \n",
 694 |        "4         False       False   False  False       False          False   \n",
 695 |        "...         ...         ...     ...    ...         ...            ...   \n",
 696 |        "537572    False       False   False  False       False          False   \n",
 697 |        "537573    False       False   False  False       False          False   \n",
 698 |        "537574    False       False   False  False       False          False   \n",
 699 |        "537575    False       False   False  False       False          False   \n",
 700 |        "537576    False       False   False  False       False          False   \n",
 701 |        "\n",
 702 |        "        Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
 703 |        "0                            False           False               False   \n",
 704 |        "1                            False           False               False   \n",
 705 |        "2                            False           False               False   \n",
 706 |        "3                            False           False               False   \n",
 707 |        "4                            False           False               False   \n",
 708 |        "...                            ...             ...                 ...   \n",
 709 |        "537572                       False           False               False   \n",
 710 |        "537573                       False           False               False   \n",
 711 |        "537574                       False           False               False   \n",
 712 |        "537575                       False           False               False   \n",
 713 |        "537576                       False           False               False   \n",
 714 |        "\n",
 715 |        "        Product_Category_2  Product_Category_3  Purchase  \n",
 716 |        "0                     True                True     False  \n",
 717 |        "1                    False               False     False  \n",
 718 |        "2                     True                True     False  \n",
 719 |        "3                    False                True     False  \n",
 720 |        "4                     True                True     False  \n",
 721 |        "...                    ...                 ...       ...  \n",
 722 |        "537572               False                True     False  \n",
 723 |        "537573               False               False     False  \n",
 724 |        "537574               False                True     False  \n",
 725 |        "537575                True                True     False  \n",
 726 |        "537576               False                True     False  \n",
 727 |        "\n",
 728 |        "[537577 rows x 12 columns]"
 729 |       ]
 730 |      },
 731 |      "execution_count": 12,
 732 |      "metadata": {},
 733 |      "output_type": "execute_result"
 734 |     }
 735 |    ],
 736 |    "source": [
 737 |     "df.isnull()"
 738 |    ]
 739 |   },
 740 |   {
 741 |    "cell_type": "code",
 742 |    "execution_count": 14,
 743 |    "id": "457142f3-a7fd-48af-92f6-0e8ca29b6d09",
 744 |    "metadata": {},
 745 |    "outputs": [
 746 |     {
 747 |      "data": {
 748 |       "text/plain": [
 749 |        "User_ID                            0\n",
 750 |        "Product_ID                         0\n",
 751 |        "Gender                             0\n",
 752 |        "Age                                0\n",
 753 |        "Occupation                         0\n",
 754 |        "City_Category                      0\n",
 755 |        "Stay_In_Current_City_Years         0\n",
 756 |        "Marital_Status                     0\n",
 757 |        "Product_Category_1                 0\n",
 758 |        "Product_Category_2            166986\n",
 759 |        "Product_Category_3            373299\n",
 760 |        "Purchase                           0\n",
 761 |        "dtype: int64"
 762 |       ]
 763 |      },
 764 |      "execution_count": 14,
 765 |      "metadata": {},
 766 |      "output_type": "execute_result"
 767 |     }
 768 |    ],
 769 |    "source": [
 770 |     "df.isnull().sum()"
 771 |    ]
 772 |   },
 773 |   {
 774 |    "cell_type": "code",
 775 |    "execution_count": 16,
 776 |    "id": "3687a103-9856-4c86-b551-eb43d9ebdb7f",
 777 |    "metadata": {},
 778 |    "outputs": [
 779 |     {
 780 |      "data": {
 781 |       "text/html": [
 782 |        "<div>\n",
 783 |        "<style scoped>\n",
 784 |        "    .dataframe tbody tr th:only-of-type {\n",
 785 |        "        vertical-align: middle;\n",
 786 |        "    }\n",
 787 |        "\n",
 788 |        "    .dataframe tbody tr th {\n",
 789 |        "        vertical-align: top;\n",
 790 |        "    }\n",
 791 |        "\n",
 792 |        "    .dataframe thead th {\n",
 793 |        "        text-align: right;\n",
 794 |        "    }\n",
 795 |        "</style>\n",
 796 |        "<table border=\"1\" class=\"dataframe\">\n",
 797 |        "  <thead>\n",
 798 |        "    <tr style=\"text-align: right;\">\n",
 799 |        "      <th></th>\n",
 800 |        "      <th>User_ID</th>\n",
 801 |        "      <th>Product_ID</th>\n",
 802 |        "      <th>Gender</th>\n",
 803 |        "      <th>Age</th>\n",
 804 |        "      <th>Occupation</th>\n",
 805 |        "      <th>City_Category</th>\n",
 806 |        "      <th>Stay_In_Current_City_Years</th>\n",
 807 |        "      <th>Marital_Status</th>\n",
 808 |        "      <th>Product_Category_1</th>\n",
 809 |        "      <th>Product_Category_2</th>\n",
 810 |        "      <th>Product_Category_3</th>\n",
 811 |        "      <th>Purchase</th>\n",
 812 |        "    </tr>\n",
 813 |        "  </thead>\n",
 814 |        "  <tbody>\n",
 815 |        "    <tr>\n",
 816 |        "      <th>1</th>\n",
 817 |        "      <td>1000001</td>\n",
 818 |        "      <td>P00248942</td>\n",
 819 |        "      <td>F</td>\n",
 820 |        "      <td>0-17</td>\n",
 821 |        "      <td>10</td>\n",
 822 |        "      <td>A</td>\n",
 823 |        "      <td>2</td>\n",
 824 |        "      <td>0</td>\n",
 825 |        "      <td>1</td>\n",
 826 |        "      <td>6.0</td>\n",
 827 |        "      <td>14.0</td>\n",
 828 |        "      <td>15200</td>\n",
 829 |        "    </tr>\n",
 830 |        "    <tr>\n",
 831 |        "      <th>6</th>\n",
 832 |        "      <td>1000004</td>\n",
 833 |        "      <td>P00184942</td>\n",
 834 |        "      <td>M</td>\n",
 835 |        "      <td>46-50</td>\n",
 836 |        "      <td>7</td>\n",
 837 |        "      <td>B</td>\n",
 838 |        "      <td>2</td>\n",
 839 |        "      <td>1</td>\n",
 840 |        "      <td>1</td>\n",
 841 |        "      <td>8.0</td>\n",
 842 |        "      <td>17.0</td>\n",
 843 |        "      <td>19215</td>\n",
 844 |        "    </tr>\n",
 845 |        "    <tr>\n",
 846 |        "      <th>13</th>\n",
 847 |        "      <td>1000005</td>\n",
 848 |        "      <td>P00145042</td>\n",
 849 |        "      <td>M</td>\n",
 850 |        "      <td>26-35</td>\n",
 851 |        "      <td>20</td>\n",
 852 |        "      <td>A</td>\n",
 853 |        "      <td>1</td>\n",
 854 |        "      <td>1</td>\n",
 855 |        "      <td>1</td>\n",
 856 |        "      <td>2.0</td>\n",
 857 |        "      <td>5.0</td>\n",
 858 |        "      <td>15665</td>\n",
 859 |        "    </tr>\n",
 860 |        "    <tr>\n",
 861 |        "      <th>14</th>\n",
 862 |        "      <td>1000006</td>\n",
 863 |        "      <td>P00231342</td>\n",
 864 |        "      <td>F</td>\n",
 865 |        "      <td>51-55</td>\n",
 866 |        "      <td>9</td>\n",
 867 |        "      <td>A</td>\n",
 868 |        "      <td>1</td>\n",
 869 |        "      <td>0</td>\n",
 870 |        "      <td>5</td>\n",
 871 |        "      <td>8.0</td>\n",
 872 |        "      <td>14.0</td>\n",
 873 |        "      <td>5378</td>\n",
 874 |        "    </tr>\n",
 875 |        "    <tr>\n",
 876 |        "      <th>16</th>\n",
 877 |        "      <td>1000006</td>\n",
 878 |        "      <td>P0096642</td>\n",
 879 |        "      <td>F</td>\n",
 880 |        "      <td>51-55</td>\n",
 881 |        "      <td>9</td>\n",
 882 |        "      <td>A</td>\n",
 883 |        "      <td>1</td>\n",
 884 |        "      <td>0</td>\n",
 885 |        "      <td>2</td>\n",
 886 |        "      <td>3.0</td>\n",
 887 |        "      <td>4.0</td>\n",
 888 |        "      <td>13055</td>\n",
 889 |        "    </tr>\n",
 890 |        "    <tr>\n",
 891 |        "      <th>...</th>\n",
 892 |        "      <td>...</td>\n",
 893 |        "      <td>...</td>\n",
 894 |        "      <td>...</td>\n",
 895 |        "      <td>...</td>\n",
 896 |        "      <td>...</td>\n",
 897 |        "      <td>...</td>\n",
 898 |        "      <td>...</td>\n",
 899 |        "      <td>...</td>\n",
 900 |        "      <td>...</td>\n",
 901 |        "      <td>...</td>\n",
 902 |        "      <td>...</td>\n",
 903 |        "      <td>...</td>\n",
 904 |        "    </tr>\n",
 905 |        "    <tr>\n",
 906 |        "      <th>537549</th>\n",
 907 |        "      <td>1004734</td>\n",
 908 |        "      <td>P00345842</td>\n",
 909 |        "      <td>M</td>\n",
 910 |        "      <td>51-55</td>\n",
 911 |        "      <td>1</td>\n",
 912 |        "      <td>B</td>\n",
 913 |        "      <td>1</td>\n",
 914 |        "      <td>1</td>\n",
 915 |        "      <td>2</td>\n",
 916 |        "      <td>8.0</td>\n",
 917 |        "      <td>14.0</td>\n",
 918 |        "      <td>13082</td>\n",
 919 |        "    </tr>\n",
 920 |        "    <tr>\n",
 921 |        "      <th>537551</th>\n",
 922 |        "      <td>1004735</td>\n",
 923 |        "      <td>P00313442</td>\n",
 924 |        "      <td>M</td>\n",
 925 |        "      <td>46-50</td>\n",
 926 |        "      <td>3</td>\n",
 927 |        "      <td>C</td>\n",
 928 |        "      <td>3</td>\n",
 929 |        "      <td>0</td>\n",
 930 |        "      <td>5</td>\n",
 931 |        "      <td>6.0</td>\n",
 932 |        "      <td>8.0</td>\n",
 933 |        "      <td>6863</td>\n",
 934 |        "    </tr>\n",
 935 |        "    <tr>\n",
 936 |        "      <th>537562</th>\n",
 937 |        "      <td>1004736</td>\n",
 938 |        "      <td>P00146742</td>\n",
 939 |        "      <td>M</td>\n",
 940 |        "      <td>18-25</td>\n",
 941 |        "      <td>20</td>\n",
 942 |        "      <td>A</td>\n",
 943 |        "      <td>1</td>\n",
 944 |        "      <td>1</td>\n",
 945 |        "      <td>1</td>\n",
 946 |        "      <td>13.0</td>\n",
 947 |        "      <td>14.0</td>\n",
 948 |        "      <td>11508</td>\n",
 949 |        "    </tr>\n",
 950 |        "    <tr>\n",
 951 |        "      <th>537571</th>\n",
 952 |        "      <td>1004737</td>\n",
 953 |        "      <td>P00221442</td>\n",
 954 |        "      <td>M</td>\n",
 955 |        "      <td>36-45</td>\n",
 956 |        "      <td>16</td>\n",
 957 |        "      <td>C</td>\n",
 958 |        "      <td>1</td>\n",
 959 |        "      <td>0</td>\n",
 960 |        "      <td>1</td>\n",
 961 |        "      <td>2.0</td>\n",
 962 |        "      <td>5.0</td>\n",
 963 |        "      <td>11852</td>\n",
 964 |        "    </tr>\n",
 965 |        "    <tr>\n",
 966 |        "      <th>537573</th>\n",
 967 |        "      <td>1004737</td>\n",
 968 |        "      <td>P00111142</td>\n",
 969 |        "      <td>M</td>\n",
 970 |        "      <td>36-45</td>\n",
 971 |        "      <td>16</td>\n",
 972 |        "      <td>C</td>\n",
 973 |        "      <td>1</td>\n",
 974 |        "      <td>0</td>\n",
 975 |        "      <td>1</td>\n",
 976 |        "      <td>15.0</td>\n",
 977 |        "      <td>16.0</td>\n",
 978 |        "      <td>19196</td>\n",
 979 |        "    </tr>\n",
 980 |        "  </tbody>\n",
 981 |        "</table>\n",
 982 |        "<p>164278 rows × 12 columns</p>\n",
 983 |        "</div>"
 984 |       ],
 985 |       "text/plain": [
 986 |        "        User_ID Product_ID Gender    Age  Occupation City_Category  \\\n",
 987 |        "1       1000001  P00248942      F   0-17          10             A   \n",
 988 |        "6       1000004  P00184942      M  46-50           7             B   \n",
 989 |        "13      1000005  P00145042      M  26-35          20             A   \n",
 990 |        "14      1000006  P00231342      F  51-55           9             A   \n",
 991 |        "16      1000006   P0096642      F  51-55           9             A   \n",
 992 |        "...         ...        ...    ...    ...         ...           ...   \n",
 993 |        "537549  1004734  P00345842      M  51-55           1             B   \n",
 994 |        "537551  1004735  P00313442      M  46-50           3             C   \n",
 995 |        "537562  1004736  P00146742      M  18-25          20             A   \n",
 996 |        "537571  1004737  P00221442      M  36-45          16             C   \n",
 997 |        "537573  1004737  P00111142      M  36-45          16             C   \n",
 998 |        "\n",
 999 |        "       Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
1000 |        "1                               2               0                   1   \n",
1001 |        "6                               2               1                   1   \n",
1002 |        "13                              1               1                   1   \n",
1003 |        "14                              1               0                   5   \n",
1004 |        "16                              1               0                   2   \n",
1005 |        "...                           ...             ...                 ...   \n",
1006 |        "537549                          1               1                   2   \n",
1007 |        "537551                          3               0                   5   \n",
1008 |        "537562                          1               1                   1   \n",
1009 |        "537571                          1               0                   1   \n",
1010 |        "537573                          1               0                   1   \n",
1011 |        "\n",
1012 |        "        Product_Category_2  Product_Category_3  Purchase  \n",
1013 |        "1                      6.0                14.0     15200  \n",
1014 |        "6                      8.0                17.0     19215  \n",
1015 |        "13                     2.0                 5.0     15665  \n",
1016 |        "14                     8.0                14.0      5378  \n",
1017 |        "16                     3.0                 4.0     13055  \n",
1018 |        "...                    ...                 ...       ...  \n",
1019 |        "537549                 8.0                14.0     13082  \n",
1020 |        "537551                 6.0                 8.0      6863  \n",
1021 |        "537562                13.0                14.0     11508  \n",
1022 |        "537571                 2.0                 5.0     11852  \n",
1023 |        "537573                15.0                16.0     19196  \n",
1024 |        "\n",
1025 |        "[164278 rows x 12 columns]"
1026 |       ]
1027 |      },
1028 |      "execution_count": 16,
1029 |      "metadata": {},
1030 |      "output_type": "execute_result"
1031 |     }
1032 |    ],
1033 |    "source": [
1034 |     "df.dropna()"
1035 |    ]
1036 |   },
1037 |   {
1038 |    "cell_type": "code",
1039 |    "execution_count": 18,
1040 |    "id": "041a5a67-2320-47c3-b366-f66edfe48bf6",
1041 |    "metadata": {},
1042 |    "outputs": [],
1043 |    "source": [
1044 |     "\n",
1045 |     "del df['Product_Category_2']\n",
1046 |     "del df['Product_Category_3']"
1047 |    ]
1048 |   },
1049 |   {
1050 |    "cell_type": "code",
1051 |    "execution_count": 20,
1052 |    "id": "42cdbb5a-3878-46ee-845d-347b13452916",
1053 |    "metadata": {},
1054 |    "outputs": [
1055 |     {
1056 |      "data": {
1057 |       "text/html": [
1058 |        "<div>\n",
1059 |        "<style scoped>\n",
1060 |        "    .dataframe tbody tr th:only-of-type {\n",
1061 |        "        vertical-align: middle;\n",
1062 |        "    }\n",
1063 |        "\n",
1064 |        "    .dataframe tbody tr th {\n",
1065 |        "        vertical-align: top;\n",
1066 |        "    }\n",
1067 |        "\n",
1068 |        "    .dataframe thead th {\n",
1069 |        "        text-align: right;\n",
1070 |        "    }\n",
1071 |        "</style>\n",
1072 |        "<table border=\"1\" class=\"dataframe\">\n",
1073 |        "  <thead>\n",
1074 |        "    <tr style=\"text-align: right;\">\n",
1075 |        "      <th></th>\n",
1076 |        "      <th>User_ID</th>\n",
1077 |        "      <th>Product_ID</th>\n",
1078 |        "      <th>Gender</th>\n",
1079 |        "      <th>Age</th>\n",
1080 |        "      <th>Occupation</th>\n",
1081 |        "      <th>City_Category</th>\n",
1082 |        "      <th>Stay_In_Current_City_Years</th>\n",
1083 |        "      <th>Marital_Status</th>\n",
1084 |        "      <th>Product_Category_1</th>\n",
1085 |        "      <th>Purchase</th>\n",
1086 |        "    </tr>\n",
1087 |        "  </thead>\n",
1088 |        "  <tbody>\n",
1089 |        "    <tr>\n",
1090 |        "      <th>0</th>\n",
1091 |        "      <td>1000001</td>\n",
1092 |        "      <td>P00069042</td>\n",
1093 |        "      <td>F</td>\n",
1094 |        "      <td>0-17</td>\n",
1095 |        "      <td>10</td>\n",
1096 |        "      <td>A</td>\n",
1097 |        "      <td>2</td>\n",
1098 |        "      <td>0</td>\n",
1099 |        "      <td>3</td>\n",
1100 |        "      <td>8370</td>\n",
1101 |        "    </tr>\n",
1102 |        "    <tr>\n",
1103 |        "      <th>1</th>\n",
1104 |        "      <td>1000001</td>\n",
1105 |        "      <td>P00248942</td>\n",
1106 |        "      <td>F</td>\n",
1107 |        "      <td>0-17</td>\n",
1108 |        "      <td>10</td>\n",
1109 |        "      <td>A</td>\n",
1110 |        "      <td>2</td>\n",
1111 |        "      <td>0</td>\n",
1112 |        "      <td>1</td>\n",
1113 |        "      <td>15200</td>\n",
1114 |        "    </tr>\n",
1115 |        "    <tr>\n",
1116 |        "      <th>2</th>\n",
1117 |        "      <td>1000001</td>\n",
1118 |        "      <td>P00087842</td>\n",
1119 |        "      <td>F</td>\n",
1120 |        "      <td>0-17</td>\n",
1121 |        "      <td>10</td>\n",
1122 |        "      <td>A</td>\n",
1123 |        "      <td>2</td>\n",
1124 |        "      <td>0</td>\n",
1125 |        "      <td>12</td>\n",
1126 |        "      <td>1422</td>\n",
1127 |        "    </tr>\n",
1128 |        "    <tr>\n",
1129 |        "      <th>3</th>\n",
1130 |        "      <td>1000001</td>\n",
1131 |        "      <td>P00085442</td>\n",
1132 |        "      <td>F</td>\n",
1133 |        "      <td>0-17</td>\n",
1134 |        "      <td>10</td>\n",
1135 |        "      <td>A</td>\n",
1136 |        "      <td>2</td>\n",
1137 |        "      <td>0</td>\n",
1138 |        "      <td>12</td>\n",
1139 |        "      <td>1057</td>\n",
1140 |        "    </tr>\n",
1141 |        "    <tr>\n",
1142 |        "      <th>4</th>\n",
1143 |        "      <td>1000002</td>\n",
1144 |        "      <td>P00285442</td>\n",
1145 |        "      <td>M</td>\n",
1146 |        "      <td>55+</td>\n",
1147 |        "      <td>16</td>\n",
1148 |        "      <td>C</td>\n",
1149 |        "      <td>4+</td>\n",
1150 |        "      <td>0</td>\n",
1151 |        "      <td>8</td>\n",
1152 |        "      <td>7969</td>\n",
1153 |        "    </tr>\n",
1154 |        "    <tr>\n",
1155 |        "      <th>...</th>\n",
1156 |        "      <td>...</td>\n",
1157 |        "      <td>...</td>\n",
1158 |        "      <td>...</td>\n",
1159 |        "      <td>...</td>\n",
1160 |        "      <td>...</td>\n",
1161 |        "      <td>...</td>\n",
1162 |        "      <td>...</td>\n",
1163 |        "      <td>...</td>\n",
1164 |        "      <td>...</td>\n",
1165 |        "      <td>...</td>\n",
1166 |        "    </tr>\n",
1167 |        "    <tr>\n",
1168 |        "      <th>537572</th>\n",
1169 |        "      <td>1004737</td>\n",
1170 |        "      <td>P00193542</td>\n",
1171 |        "      <td>M</td>\n",
1172 |        "      <td>36-45</td>\n",
1173 |        "      <td>16</td>\n",
1174 |        "      <td>C</td>\n",
1175 |        "      <td>1</td>\n",
1176 |        "      <td>0</td>\n",
1177 |        "      <td>1</td>\n",
1178 |        "      <td>11664</td>\n",
1179 |        "    </tr>\n",
1180 |        "    <tr>\n",
1181 |        "      <th>537573</th>\n",
1182 |        "      <td>1004737</td>\n",
1183 |        "      <td>P00111142</td>\n",
1184 |        "      <td>M</td>\n",
1185 |        "      <td>36-45</td>\n",
1186 |        "      <td>16</td>\n",
1187 |        "      <td>C</td>\n",
1188 |        "      <td>1</td>\n",
1189 |        "      <td>0</td>\n",
1190 |        "      <td>1</td>\n",
1191 |        "      <td>19196</td>\n",
1192 |        "    </tr>\n",
1193 |        "    <tr>\n",
1194 |        "      <th>537574</th>\n",
1195 |        "      <td>1004737</td>\n",
1196 |        "      <td>P00345942</td>\n",
1197 |        "      <td>M</td>\n",
1198 |        "      <td>36-45</td>\n",
1199 |        "      <td>16</td>\n",
1200 |        "      <td>C</td>\n",
1201 |        "      <td>1</td>\n",
1202 |        "      <td>0</td>\n",
1203 |        "      <td>8</td>\n",
1204 |        "      <td>8043</td>\n",
1205 |        "    </tr>\n",
1206 |        "    <tr>\n",
1207 |        "      <th>537575</th>\n",
1208 |        "      <td>1004737</td>\n",
1209 |        "      <td>P00285842</td>\n",
1210 |        "      <td>M</td>\n",
1211 |        "      <td>36-45</td>\n",
1212 |        "      <td>16</td>\n",
1213 |        "      <td>C</td>\n",
1214 |        "      <td>1</td>\n",
1215 |        "      <td>0</td>\n",
1216 |        "      <td>5</td>\n",
1217 |        "      <td>7172</td>\n",
1218 |        "    </tr>\n",
1219 |        "    <tr>\n",
1220 |        "      <th>537576</th>\n",
1221 |        "      <td>1004737</td>\n",
1222 |        "      <td>P00118242</td>\n",
1223 |        "      <td>M</td>\n",
1224 |        "      <td>36-45</td>\n",
1225 |        "      <td>16</td>\n",
1226 |        "      <td>C</td>\n",
1227 |        "      <td>1</td>\n",
1228 |        "      <td>0</td>\n",
1229 |        "      <td>5</td>\n",
1230 |        "      <td>6875</td>\n",
1231 |        "    </tr>\n",
1232 |        "  </tbody>\n",
1233 |        "</table>\n",
1234 |        "<p>537577 rows × 10 columns</p>\n",
1235 |        "</div>"
1236 |       ],
1237 |       "text/plain": [
1238 |        "        User_ID Product_ID Gender    Age  Occupation City_Category  \\\n",
1239 |        "0       1000001  P00069042      F   0-17          10             A   \n",
1240 |        "1       1000001  P00248942      F   0-17          10             A   \n",
1241 |        "2       1000001  P00087842      F   0-17          10             A   \n",
1242 |        "3       1000001  P00085442      F   0-17          10             A   \n",
1243 |        "4       1000002  P00285442      M    55+          16             C   \n",
1244 |        "...         ...        ...    ...    ...         ...           ...   \n",
1245 |        "537572  1004737  P00193542      M  36-45          16             C   \n",
1246 |        "537573  1004737  P00111142      M  36-45          16             C   \n",
1247 |        "537574  1004737  P00345942      M  36-45          16             C   \n",
1248 |        "537575  1004737  P00285842      M  36-45          16             C   \n",
1249 |        "537576  1004737  P00118242      M  36-45          16             C   \n",
1250 |        "\n",
1251 |        "       Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
1252 |        "0                               2               0                   3   \n",
1253 |        "1                               2               0                   1   \n",
1254 |        "2                               2               0                  12   \n",
1255 |        "3                               2               0                  12   \n",
1256 |        "4                              4+               0                   8   \n",
1257 |        "...                           ...             ...                 ...   \n",
1258 |        "537572                          1               0                   1   \n",
1259 |        "537573                          1               0                   1   \n",
1260 |        "537574                          1               0                   8   \n",
1261 |        "537575                          1               0                   5   \n",
1262 |        "537576                          1               0                   5   \n",
1263 |        "\n",
1264 |        "        Purchase  \n",
1265 |        "0           8370  \n",
1266 |        "1          15200  \n",
1267 |        "2           1422  \n",
1268 |        "3           1057  \n",
1269 |        "4           7969  \n",
1270 |        "...          ...  \n",
1271 |        "537572     11664  \n",
1272 |        "537573     19196  \n",
1273 |        "537574      8043  \n",
1274 |        "537575      7172  \n",
1275 |        "537576      6875  \n",
1276 |        "\n",
1277 |        "[537577 rows x 10 columns]"
1278 |       ]
1279 |      },
1280 |      "execution_count": 20,
1281 |      "metadata": {},
1282 |      "output_type": "execute_result"
1283 |     }
1284 |    ],
1285 |    "source": [
1286 |     "df"
1287 |    ]
1288 |   },
1289 |   {
1290 |    "cell_type": "code",
1291 |    "execution_count": null,
1292 |    "id": "b85f15c0-ea53-445f-aab5-cf25edf5287b",
1293 |    "metadata": {},
1294 |    "outputs": [],
1295 |    "source": []
1296 |   }
1297 |  ],
1298 |  "metadata": {
1299 |   "kernelspec": {
1300 |    "display_name": "Python 3 (ipykernel)",
1301 |    "language": "python",
1302 |    "name": "python3"
1303 |   },
1304 |   "language_info": {
1305 |    "codemirror_mode": {
1306 |     "name": "ipython",
1307 |     "version": 3
1308 |    },
1309 |    "file_extension": ".py",
1310 |    "mimetype": "text/x-python",
1311 |    "name": "python",
1312 |    "nbconvert_exporter": "python",
1313 |    "pygments_lexer": "ipython3",
1314 |    "version": "3.12.4"
1315 |   }
1316 |  },
1317 |  "nbformat": 4,
1318 |  "nbformat_minor": 5
1319 | }
1320 | 


--------------------------------------------------------------------------------
/Black Friday -  Analysing Age & Marital Status.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 76,
  6 |    "id": "1c0acf39",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import pandas as pd"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 77,
 16 |    "id": "d1720a06",
 17 |    "metadata": {},
 18 |    "outputs": [],
 19 |    "source": [
 20 |     "df = pd.read_csv('BlackFriday.csv')\n",
 21 |     "\n",
 22 |     "del df['Product_Category_2']\n",
 23 |     "del df['Product_Category_3']"
 24 |    ]
 25 |   },
 26 |   {
 27 |    "cell_type": "code",
 28 |    "execution_count": 78,
 29 |    "id": "9b992a81",
 30 |    "metadata": {},
 31 |    "outputs": [
 32 |     {
 33 |      "data": {
 34 |       "text/html": [
 35 |        "<div>\n",
 36 |        "<style scoped>\n",
 37 |        "    .dataframe tbody tr th:only-of-type {\n",
 38 |        "        vertical-align: middle;\n",
 39 |        "    }\n",
 40 |        "\n",
 41 |        "    .dataframe tbody tr th {\n",
 42 |        "        vertical-align: top;\n",
 43 |        "    }\n",
 44 |        "\n",
 45 |        "    .dataframe thead th {\n",
 46 |        "        text-align: right;\n",
 47 |        "    }\n",
 48 |        "</style>\n",
 49 |        "<table border=\"1\" class=\"dataframe\">\n",
 50 |        "  <thead>\n",
 51 |        "    <tr style=\"text-align: right;\">\n",
 52 |        "      <th></th>\n",
 53 |        "      <th>User_ID</th>\n",
 54 |        "      <th>Product_ID</th>\n",
 55 |        "      <th>Gender</th>\n",
 56 |        "      <th>Age</th>\n",
 57 |        "      <th>Occupation</th>\n",
 58 |        "      <th>City_Category</th>\n",
 59 |        "      <th>Stay_In_Current_City_Years</th>\n",
 60 |        "      <th>Marital_Status</th>\n",
 61 |        "      <th>Product_Category_1</th>\n",
 62 |        "      <th>Purchase</th>\n",
 63 |        "    </tr>\n",
 64 |        "  </thead>\n",
 65 |        "  <tbody>\n",
 66 |        "    <tr>\n",
 67 |        "      <th>0</th>\n",
 68 |        "      <td>1000001</td>\n",
 69 |        "      <td>P00069042</td>\n",
 70 |        "      <td>F</td>\n",
 71 |        "      <td>0-17</td>\n",
 72 |        "      <td>10</td>\n",
 73 |        "      <td>A</td>\n",
 74 |        "      <td>2</td>\n",
 75 |        "      <td>0</td>\n",
 76 |        "      <td>3</td>\n",
 77 |        "      <td>8370</td>\n",
 78 |        "    </tr>\n",
 79 |        "    <tr>\n",
 80 |        "      <th>1</th>\n",
 81 |        "      <td>1000001</td>\n",
 82 |        "      <td>P00248942</td>\n",
 83 |        "      <td>F</td>\n",
 84 |        "      <td>0-17</td>\n",
 85 |        "      <td>10</td>\n",
 86 |        "      <td>A</td>\n",
 87 |        "      <td>2</td>\n",
 88 |        "      <td>0</td>\n",
 89 |        "      <td>1</td>\n",
 90 |        "      <td>15200</td>\n",
 91 |        "    </tr>\n",
 92 |        "    <tr>\n",
 93 |        "      <th>2</th>\n",
 94 |        "      <td>1000001</td>\n",
 95 |        "      <td>P00087842</td>\n",
 96 |        "      <td>F</td>\n",
 97 |        "      <td>0-17</td>\n",
 98 |        "      <td>10</td>\n",
 99 |        "      <td>A</td>\n",
100 |        "      <td>2</td>\n",
101 |        "      <td>0</td>\n",
102 |        "      <td>12</td>\n",
103 |        "      <td>1422</td>\n",
104 |        "    </tr>\n",
105 |        "    <tr>\n",
106 |        "      <th>3</th>\n",
107 |        "      <td>1000001</td>\n",
108 |        "      <td>P00085442</td>\n",
109 |        "      <td>F</td>\n",
110 |        "      <td>0-17</td>\n",
111 |        "      <td>10</td>\n",
112 |        "      <td>A</td>\n",
113 |        "      <td>2</td>\n",
114 |        "      <td>0</td>\n",
115 |        "      <td>12</td>\n",
116 |        "      <td>1057</td>\n",
117 |        "    </tr>\n",
118 |        "    <tr>\n",
119 |        "      <th>4</th>\n",
120 |        "      <td>1000002</td>\n",
121 |        "      <td>P00285442</td>\n",
122 |        "      <td>M</td>\n",
123 |        "      <td>55+</td>\n",
124 |        "      <td>16</td>\n",
125 |        "      <td>C</td>\n",
126 |        "      <td>4+</td>\n",
127 |        "      <td>0</td>\n",
128 |        "      <td>8</td>\n",
129 |        "      <td>7969</td>\n",
130 |        "    </tr>\n",
131 |        "  </tbody>\n",
132 |        "</table>\n",
133 |        "</div>"
134 |       ],
135 |       "text/plain": [
136 |        "   User_ID Product_ID Gender   Age  Occupation City_Category  \\\n",
137 |        "0  1000001  P00069042      F  0-17          10             A   \n",
138 |        "1  1000001  P00248942      F  0-17          10             A   \n",
139 |        "2  1000001  P00087842      F  0-17          10             A   \n",
140 |        "3  1000001  P00085442      F  0-17          10             A   \n",
141 |        "4  1000002  P00285442      M   55+          16             C   \n",
142 |        "\n",
143 |        "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  Purchase  \n",
144 |        "0                          2               0                   3      8370  \n",
145 |        "1                          2               0                   1     15200  \n",
146 |        "2                          2               0                  12      1422  \n",
147 |        "3                          2               0                  12      1057  \n",
148 |        "4                         4+               0                   8      7969  "
149 |       ]
150 |      },
151 |      "execution_count": 78,
152 |      "metadata": {},
153 |      "output_type": "execute_result"
154 |     }
155 |    ],
156 |    "source": [
157 |     "df.head()"
158 |    ]
159 |   },
160 |   {
161 |    "cell_type": "code",
162 |    "execution_count": 81,
163 |    "id": "f0144760",
164 |    "metadata": {},
165 |    "outputs": [
166 |     {
167 |      "data": {
168 |       "text/plain": [
169 |        "<AxesSubplot:title={'center':'Purchase Distribution by Age'}, xlabel='Age'>"
170 |       ]
171 |      },
172 |      "execution_count": 81,
173 |      "metadata": {},
174 |      "output_type": "execute_result"
175 |     },
176 |     {
177 |      "data": {
178 |       "image/png": 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\n",
179 |       "text/plain": [
180 |        "<Figure size 864x432 with 1 Axes>"
181 |       ]
182 |      },
183 |      "metadata": {
184 |       "needs_background": "light"
185 |      },
186 |      "output_type": "display_data"
187 |     }
188 |    ],
189 |    "source": [
190 |     "df.groupby('Age').size().plot(kind = 'bar', figsize = (12, 6), title = 'Purchase Distribution by Age')"
191 |    ]
192 |   },
193 |   {
194 |    "cell_type": "code",
195 |    "execution_count": 106,
196 |    "id": "7ab48797",
197 |    "metadata": {},
198 |    "outputs": [],
199 |    "source": [
200 |     "lst = []\n",
201 |     "for i in df['Age'].unique():\n",
202 |     "    lst.append([i, df[df['Age'] == i]['Product_ID'].nunique()])\n",
203 |     "    \n",
204 |     "data = pd.DataFrame(lst , columns = ['Age','Products'])"
205 |    ]
206 |   },
207 |   {
208 |    "cell_type": "code",
209 |    "execution_count": 113,
210 |    "id": "f274a258",
211 |    "metadata": {},
212 |    "outputs": [
213 |     {
214 |      "data": {
215 |       "text/plain": [
216 |        "<AxesSubplot:xlabel='Age'>"
217 |       ]
218 |      },
219 |      "execution_count": 113,
220 |      "metadata": {},
221 |      "output_type": "execute_result"
222 |     },
223 |     {
224 |      "data": {
225 |       "image/png": 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\n",
226 |       "text/plain": [
227 |        "<Figure size 576x432 with 1 Axes>"
228 |       ]
229 |      },
230 |      "metadata": {
231 |       "needs_background": "light"
232 |      },
233 |      "output_type": "display_data"
234 |     }
235 |    ],
236 |    "source": [
237 |     "data.plot.bar(x = 'Age', figsize = (8,6))"
238 |    ]
239 |   },
240 |   {
241 |    "cell_type": "code",
242 |    "execution_count": 115,
243 |    "id": "8947785f",
244 |    "metadata": {},
245 |    "outputs": [
246 |     {
247 |      "data": {
248 |       "text/plain": [
249 |        "<AxesSubplot:title={'center':'Amount Spend by Age'}, xlabel='Age'>"
250 |       ]
251 |      },
252 |      "execution_count": 115,
253 |      "metadata": {},
254 |      "output_type": "execute_result"
255 |     },
256 |     {
257 |      "data": {
258 |       "image/png": 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\n",
259 |       "text/plain": [
260 |        "<Figure size 864x432 with 1 Axes>"
261 |       ]
262 |      },
263 |      "metadata": {
264 |       "needs_background": "light"
265 |      },
266 |      "output_type": "display_data"
267 |     }
268 |    ],
269 |    "source": [
270 |     "df.groupby('Age').sum()['Purchase'].plot(kind = 'bar', figsize = (12, 6), title = 'Amount Spend by Age')"
271 |    ]
272 |   },
273 |   {
274 |    "cell_type": "code",
275 |    "execution_count": 116,
276 |    "id": "73aed420",
277 |    "metadata": {},
278 |    "outputs": [
279 |     {
280 |      "data": {
281 |       "text/plain": [
282 |        "<AxesSubplot:title={'center':'Amount Spend by Age'}, xlabel='Age'>"
283 |       ]
284 |      },
285 |      "execution_count": 116,
286 |      "metadata": {},
287 |      "output_type": "execute_result"
288 |     },
289 |     {
290 |      "data": {
291 |       "image/png": 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\n",
292 |       "text/plain": [
293 |        "<Figure size 864x432 with 1 Axes>"
294 |       ]
295 |      },
296 |      "metadata": {
297 |       "needs_background": "light"
298 |      },
299 |      "output_type": "display_data"
300 |     }
301 |    ],
302 |    "source": [
303 |     "df.groupby('Age').mean()['Purchase'].plot(kind = 'bar', figsize = (12, 6), title = 'Amount Spend by Age')"
304 |    ]
305 |   },
306 |   {
307 |    "cell_type": "code",
308 |    "execution_count": 119,
309 |    "id": "bbdb3f43",
310 |    "metadata": {},
311 |    "outputs": [
312 |     {
313 |      "data": {
314 |       "text/plain": [
315 |        "<AxesSubplot:ylabel='Purchase'>"
316 |       ]
317 |      },
318 |      "execution_count": 119,
319 |      "metadata": {},
320 |      "output_type": "execute_result"
321 |     },
322 |     {
323 |      "data": {
324 |       "image/png": 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\n",
325 |       "text/plain": [
326 |        "<Figure size 432x288 with 1 Axes>"
327 |       ]
328 |      },
329 |      "metadata": {},
330 |      "output_type": "display_data"
331 |     }
332 |    ],
333 |    "source": [
334 |     "df.groupby('Age').mean()['Purchase'].plot(kind = 'pie', autopct = '%0.1f')"
335 |    ]
336 |   },
337 |   {
338 |    "cell_type": "code",
339 |    "execution_count": 121,
340 |    "id": "eddf2927",
341 |    "metadata": {},
342 |    "outputs": [
343 |     {
344 |      "data": {
345 |       "text/plain": [
346 |        "<AxesSubplot:ylabel='None'>"
347 |       ]
348 |      },
349 |      "execution_count": 121,
350 |      "metadata": {},
351 |      "output_type": "execute_result"
352 |     },
353 |     {
354 |      "data": {
355 |       "image/png": 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\n",
356 |       "text/plain": [
357 |        "<Figure size 432x288 with 1 Axes>"
358 |       ]
359 |      },
360 |      "metadata": {},
361 |      "output_type": "display_data"
362 |     }
363 |    ],
364 |    "source": [
365 |     "df.groupby('Marital_Status').size().plot(kind = 'pie', autopct = '%0.1f')"
366 |    ]
367 |   },
368 |   {
369 |    "cell_type": "code",
370 |    "execution_count": 122,
371 |    "id": "fa6bc690",
372 |    "metadata": {},
373 |    "outputs": [
374 |     {
375 |      "data": {
376 |       "text/plain": [
377 |        "<AxesSubplot:ylabel='None'>"
378 |       ]
379 |      },
380 |      "execution_count": 122,
381 |      "metadata": {},
382 |      "output_type": "execute_result"
383 |     },
384 |     {
385 |      "data": {
386 |       "image/png": 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6JdbvkBo8m6UOLZQOoFQ/ipKDZ7nUj0gHUKoPAfCoZIAsl/o3wBvSIZTawWKsv1oyQHZLbf0u4D7pGErtIMm71vQpu6UOLZAOoNQOtNSj9ASwSjqEUlW/w/rLpENku9TW7wbulY6hVNWPpQNA1ksdukM6gFKAT0ommGzd9bI/1nsCOFwywl/WdfGp+7bfFWj5hm6+dVQTG0sBtz7dwS7jDABXHt3E7Hc29PkcXd0Bs27dytTxOR45fVwiuVVkbsH650qHAIG73MfkJoRLPWNyHUvOaQbCck69bgsn7d/A7UsqfOkDjXz5Q02DPseNiyscMDnHpsTvk6gi8CPpAD1cOPwGuJ8UvWG28OUu9p2YY+93DP3Lu3JTN8WlnZx9aGOMyVRMnsb6qbnO341SW78TuEU6Ro97nu/gtPdsP8T+3lMVDr55C2f9Vzsb2vt+uXPRf5e46pgx5ExSKVWErpIO0JsbpQ7NAyrSISpdAQ//pZNPvjt8ZXPurEaWXdDMknN2Ykqz4ZJfvP0W24+81MGuOxkO20N3Ps6gpaTkDbIe7pQ63BHlTukYjy3t5NApOXZrDr+0uzXnqMsZcsYw97BGnnqt622f8z8runj4L53kb9jMp+9r51cvdzLnAb0Vd0Z8p3pqNTXcKXXoSkDskjeAu3c49F69efv3+8E/d/CeXd/+Jf/2MWNYefF42i4azz2njOWj0+u56+SxieRVo7IS+Il0iB25VWrrtyE4W2/rCHh8eRcnH7C91F/9ZZmDbt7CwTdv4ddtXVx/3BgAVm3uZvb8bVJRVTSuxfriL/l25MZ56t6stxfhJuqDn0NSauRWA/th/dT9y+zWTA1g/RXAzdIxlPO+nsZCg4ulDl0BbJIOoZz1NClenuxmqa2/DviGdAzlrAsl7mY5VG6WOnQT8Ix0COWce7H+E9IhBuJuqcOdUc4FUnUOUWVaCfiqdIjBuFtqAOsvBm6VjqGccXn1tGmquV3q0KXoBoVq9J4hZWu8++N+qa2/AbhAOobKtA7grOqFQ6nnfqkBrL+AFKwLV5n1Tay/RDrEUNVGqUPnEV5Ro9RwLAJapUMMR+2U2vpbgNMRvuBDZYoPfKZ6JiUzaqfUANb/A/Cv0jFUJgTAnDRs+TtctVXq0NXAz6VDqNS7HOtn8n5ttVfqcHnfp4AXpaOo1HoM+KZ0iJFy79LLobLevsBiYJJ0FJUqy4FZ1VOhmVR7M3WP8LXSJ9A3ztR2PnBSlgsNtVxqAOv/FjhHOoZKhRJwItZ/TjrIaNV2qQGsfxsZOw+pItcFnFb9Rz7ztNQA1r8U3S2lln0B6z8kHSIqWurtvgj8h3QIlbhLsX4q7lYZFS11j/BU15nAfOkoKjEW6zv30qt2T2n1x3o5whn7DOkoKlYXY/3rpUPEwdmZ2hgTGGN+0uv/640xa40xA68SCu+28FngBzFHVDK6gbmuFhocLjWwFXiPMabnVhfHAq8N6TOt3431v0i4wYIeyrijAzgd66fmtrNxcLnUEC73a6n++jTg7mF9dvh667PoAhUXbCVcWLJAOkjcXC/1PcCnjTFjgIMJl4UOj/XvAo5H9xHPshXA4Vi/KB0kCU6XOgiC54A84Sz96IifyPoLgQ8Df40kmErS74H3Yf1npYMkxelSVz0MXMNwD713ZP0/AocB90eQSSXjB8CR1dsc14x66QAJuA3wgyD4ozHmyFE9k/U3AadgvQsJr8tuGOQzlIytwHlY/w7pIBKcn6mDIFgZBMGNkT6p9W8EjiB8rabS5UngkFotNOjik9Gx3kTC2/ucLh1F0Ql8C7gya3uKRU1LHQXrfZzwgpA9paPUqBcJNwj8g3SQNHD+8DsR4V5WBwI/RBerJKlCeNnsoVro7XSmjpr1PgLMA94lHcVxjxHeUlb3ct+BljoO1msgvJTzMmBn4TSuWQ58Ces/LB0krbTUcQrfSPs6YcGbhNNk3UbC9QbXYv2ScJZU01InwXrTCLec/Qy1sTYgSn8Drge+W10noAahpU6S9fYGLgLOBpplw6TeeuA64Casv1k6TJZoqSVYb2fCXUwvAHYXTpM2bcD3gVuq9z9Tw6SllmS9JsIdVuYCHxBOI6kbeJxwrfYj1Y0q1AhpqdPCejMIr92eA+wlnCYprxDeN/w2rP+KdBhXaKnTxnoGOIqw4CcAE2UDRe4l4AHgfl0wEg8tdZpZrw54PzCbcKOGmYARzTR83cCzwEPAA1j/edk47tNSZ4n1dics95HAB4F3iubpWyfwNPA74LfAE1h/o2iiGqOlzjLrTQbeBxxafRwE7E1y13lvBv7U67EEWIT1tyY0vuqDlto14SH7NGCfXo/phLfs9XZ4jO3jGboIZ9sK4cKPddXHamAV4Y6sy4A/Yf1X4/yrqJHRUteycI36GMISd2D9TuFEKgJaaqUco9dTK+UYLbVSjtFSK+UYLbVSjtFSK+UYLbVSjtFSK+UYLbVSjtFSK+UYLbVSjtFSK+UYLbVSjtFSK+UYLbVSjtFSK+UYLbVSjtFSK+UYLbVSjtFSK+UYLbVSjtFSK+UYLbVSjtFSK+UYLbVSjtFSK+UYLbVSjtFSK+WY/weNOKeiA+fFfQAAAABJRU5ErkJggg==\n",
387 |       "text/plain": [
388 |        "<Figure size 432x288 with 1 Axes>"
389 |       ]
390 |      },
391 |      "metadata": {},
392 |      "output_type": "display_data"
393 |     }
394 |    ],
395 |    "source": [
396 |     "df.groupby('Gender').size().plot(kind = 'pie', autopct = '%0.1f')"
397 |    ]
398 |   },
399 |   {
400 |    "cell_type": "code",
401 |    "execution_count": null,
402 |    "id": "eb343dac",
403 |    "metadata": {},
404 |    "outputs": [],
405 |    "source": []
406 |   }
407 |  ],
408 |  "metadata": {
409 |   "kernelspec": {
410 |    "display_name": "Python 3",
411 |    "language": "python",
412 |    "name": "python3"
413 |   },
414 |   "language_info": {
415 |    "codemirror_mode": {
416 |     "name": "ipython",
417 |     "version": 3
418 |    },
419 |    "file_extension": ".py",
420 |    "mimetype": "text/x-python",
421 |    "name": "python",
422 |    "nbconvert_exporter": "python",
423 |    "pygments_lexer": "ipython3",
424 |    "version": "3.8.8"
425 |   }
426 |  },
427 |  "nbformat": 4,
428 |  "nbformat_minor": 5
429 | }
430 | 


--------------------------------------------------------------------------------