├── requirements.txt ├── Task-by-Task Guide.pdf ├── LICENSE ├── README.md └── Clean and analyze social media usage data with Python .py.ipynb /requirements.txt: -------------------------------------------------------------------------------- 1 | pandas 2 | numpy 3 | matplotlib 4 | seaborn -------------------------------------------------------------------------------- /Task-by-Task Guide.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/venkat-0706/Social-Media-Analysis/HEAD/Task-by-Task Guide.pdf -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Coursera Project Network: Clean and analyze social media usage data with Python 2 | 3 | 4 | # The Project Scenario: 5 | 6 | In this project, you will step into the shoes of an entry-level data analyst at a social media agency, helping to create a comprehensive report that analyzes the performance of different categories of social media posts. 7 | 8 | Suppose you work for a social media marketing company that specializes in promoting brands and products on a popular social media platform. Your team is responsible for analyzing the performance of different types of posts based on categories, such as health, family, food, etc. to help clients optimize their social media strategy and increase their reach and engagement. 9 | 10 | They want you to use Python to automatically extract tweets posted from one or more categories, and to clean, analyze and visualize the data. The team will use your analysis to make data-driven recommendations to clients to improve their social media performance. This feature will help the marketing agency deliver tweets on time, within budget, and gain fast results. 11 | 12 | # Project Objectives : 13 | 14 | Increase client reach and engagement. 15 | Gain valuable insights that will help improve social media performance. 16 | Achieve their social media goals and provide data-driven recommendations. 17 | # Your Challenge: 18 | 19 | Your task will be taking on the role of a social media analyst responsible for collecting, cleaning, and analyzing data on a client's social media posts. You will also be responsible for communicating the insights and making data-driven recommendations to clients to improve their social media performance. To do this, you will set up the environment, identify the categories for the post (fitness, tech, family, beauty, etc) process, analyze, and visualize data. 20 | 21 | In this project, we'll use data from Twitter; however, to keep this project unique and open-ended, please feel free to choose whichever major social media website you'd prefer. 22 | 23 | # After you perform your analysis, you will share your findings. 24 | 25 | # See Example Projects: 26 | 27 | 1.Exploratory Data Analysis with Python: https://www.kaggle.com/code/fazilbtopal/exploratory-data-analysis-with-python 28 | 29 | 2.A Simple Tutorial on Exploratory Data Analysis: https://www.kaggle.com/code/spscientist/a-simple-tutorial-on-exploratory-data-analysis 30 | 31 | 3.Detailed exploratory data analysis with python: https://www.kaggle.com/code/ekami66/detailed-exploratory-data-analysis-with-python/notebook 32 | 33 | 4.A Simple Tutorial on Exploratory Data Analysis Python · House Prices - Advanced Regression Techniques: https://www.kaggle.com/code/spscientist/a-simple-tutorial-on-exploratory-data-analysis 34 | 35 | 5.Exploratory Data Analysis with Python Python · 1985 Automobile Dataset: https://www.kaggle.com/code/spscientist/a-simple-tutorial-on-exploratory-data-analysis 36 | 37 | # Check out these helpful resources: 38 | 1.Matplotlib tutorial: https://matplotlib.org/tutorials/introductory/pyplot.html 39 | 40 | 2.Seaborn examples: https://seaborn.pydata.org/examples/index.html 41 | 42 | 3.NumPy tutorial: https://numpy.org/doc/stable/user/quickstart.html 43 | 44 | 4.Pandas user guide: https://pandas.pydata.org/docs/user_guide/10min.html 45 | 46 | # Creating an exceptional exploratory data analysis: 47 | 48 | Data Understanding: The learner should demonstrate a deep understanding of the data and the problem being explored. The following should be included: 49 | 50 | A clear description of the data, including its source and any relevant background information 51 | 52 | A detailed exploration of the data, including summary statistics and visualizations 53 | 54 | An explanation of any data cleaning or preprocessing techniques that were applied 55 | 56 | Data Visualization: The learner should effectively use appropriate visualizations to explore the data and communicate insights. The following should be included: 57 | 58 | Clear, well-designed visualizations that effectively communicate key insights 59 | 60 | Appropriate visualizations for the type of data being analyzed 61 | 62 | Thoughtful design choices, including appropriate labeling, color choices, and formatting 63 | 64 | Analysis Techniques: The learner should use a variety of appropriate analysis techniques to explore the data and draw insights. The following should be included: 65 | 66 | A clear explanation of the analysis techniques used and why they were chosen 67 | 68 | Appropriate statistical tests, models, and algorithms to analyze the data 69 | 70 | Thoughtful consideration of the limitations and assumptions of the analysis techniques used 71 | 72 | Insights and Conclusions: The learner should draw insightful conclusions from the data and effectively communicate those conclusions. The following should be included: 73 | 74 | Clear and well-supported conclusions that provide meaningful insights into the problem being explored 75 | 76 | Appropriate recommendations or next steps based on the analysis 77 | 78 | A clear explanation of any limitations or areas for further research 79 | -------------------------------------------------------------------------------- /Clean and analyze social media usage data with Python .py.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "id": "b4cf3035", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [] 10 | }, 11 | { 12 | "cell_type": "markdown", 13 | "id": "98bde340", 14 | "metadata": {}, 15 | "source": [ 16 | "# Task-1 : Import required libraries\n" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 3, 22 | "id": "97015664", 23 | "metadata": {}, 24 | "outputs": [], 25 | "source": [ 26 | "# Task 1: Import required libraries\n", 27 | "import pandas as pd\n", 28 | "import numpy as np\n", 29 | "import matplotlib.pyplot as plt\n", 30 | "import seaborn as sns\n", 31 | "import random" 32 | ] 33 | }, 34 | { 35 | "cell_type": "markdown", 36 | "id": "d713b0fd", 37 | "metadata": {}, 38 | "source": [ 39 | "# Task-2 : Generate random data" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 8, 45 | "id": "2a77da89", 46 | "metadata": {}, 47 | "outputs": [], 48 | "source": [ 49 | "categories = ['Food', 'Travel', 'Fashion', 'Fitness', 'Music', 'Culture', 'Family', 'Health']\n", 50 | "data = {\n", 51 | " 'Date': pd.date_range('2021-01-01', periods=500),\n", 52 | " 'Category': [random.choice(categories) for _ in range(500)],\n", 53 | " 'Likes': np.random.randint(0, 10000, size=500)\n", 54 | "}" 55 | ] 56 | }, 57 | { 58 | "cell_type": "markdown", 59 | "id": "52870dae", 60 | "metadata": {}, 61 | "source": [ 62 | "# Task-3 : Load data into a Pandas DataFrame and explore" 63 | ] 64 | }, 65 | { 66 | "cell_type": "code", 67 | "execution_count": 9, 68 | "id": "d499b4df", 69 | "metadata": {}, 70 | "outputs": [], 71 | "source": [ 72 | "\n", 73 | "df = pd.DataFrame(data)" 74 | ] 75 | }, 76 | { 77 | "cell_type": "code", 78 | "execution_count": 12, 79 | "id": "da65d499", 80 | "metadata": {}, 81 | "outputs": [ 82 | { 83 | "name": "stdout", 84 | "output_type": "stream", 85 | "text": [ 86 | " Date Category Likes\n", 87 | "0 2021-01-01 Travel 8205\n", 88 | "1 2021-01-02 Food 1422\n", 89 | "2 2021-01-03 Health 2617\n", 90 | "3 2021-01-04 Health 3589\n", 91 | "4 2021-01-05 Fitness 9714\n" 92 | ] 93 | } 94 | ], 95 | "source": [ 96 | "print(df.head())" 97 | ] 98 | }, 99 | { 100 | "cell_type": "code", 101 | "execution_count": 11, 102 | "id": "aaac697b", 103 | "metadata": {}, 104 | "outputs": [ 105 | { 106 | "name": "stdout", 107 | "output_type": "stream", 108 | "text": [ 109 | "\n", 110 | "RangeIndex: 500 entries, 0 to 499\n", 111 | "Data columns (total 3 columns):\n", 112 | " # Column Non-Null Count Dtype \n", 113 | "--- ------ -------------- ----- \n", 114 | " 0 Date 500 non-null datetime64[ns]\n", 115 | " 1 Category 500 non-null object \n", 116 | " 2 Likes 500 non-null int32 \n", 117 | "dtypes: datetime64[ns](1), int32(1), object(1)\n", 118 | "memory usage: 9.9+ KB\n", 119 | "None\n" 120 | ] 121 | } 122 | ], 123 | "source": [ 124 | "print(df.info())" 125 | ] 126 | }, 127 | { 128 | "cell_type": "code", 129 | "execution_count": 13, 130 | "id": "b0233566", 131 | "metadata": {}, 132 | "outputs": [ 133 | { 134 | "name": "stdout", 135 | "output_type": "stream", 136 | "text": [ 137 | " Date Likes\n", 138 | "count 500 500.000000\n", 139 | "mean 2021-09-07 12:00:00 5062.910000\n", 140 | "min 2021-01-01 00:00:00 2.000000\n", 141 | "25% 2021-05-05 18:00:00 2541.000000\n", 142 | "50% 2021-09-07 12:00:00 4982.000000\n", 143 | "75% 2022-01-10 06:00:00 7673.000000\n", 144 | "max 2022-05-15 00:00:00 9995.000000\n", 145 | "std NaN 2911.354837\n" 146 | ] 147 | } 148 | ], 149 | "source": [ 150 | "print(df.describe())" 151 | ] 152 | }, 153 | { 154 | "cell_type": "code", 155 | "execution_count": 14, 156 | "id": "1b633dc7", 157 | "metadata": {}, 158 | "outputs": [ 159 | { 160 | "name": "stdout", 161 | "output_type": "stream", 162 | "text": [ 163 | "Category\n", 164 | "Health 86\n", 165 | "Fashion 71\n", 166 | "Food 67\n", 167 | "Travel 64\n", 168 | "Culture 59\n", 169 | "Fitness 53\n", 170 | "Music 51\n", 171 | "Family 49\n", 172 | "Name: count, dtype: int64\n" 173 | ] 174 | } 175 | ], 176 | "source": [ 177 | "print(df['Category'].value_counts())" 178 | ] 179 | }, 180 | { 181 | "cell_type": "markdown", 182 | "id": "f1714ced", 183 | "metadata": {}, 184 | "source": [ 185 | "# Task-4 : Clean the data" 186 | ] 187 | }, 188 | { 189 | "cell_type": "code", 190 | "execution_count": 21, 191 | "id": "e65c62df", 192 | "metadata": {}, 193 | "outputs": [], 194 | "source": [ 195 | "# Task 4: Clean the data\n", 196 | "df.dropna(inplace=True)\n", 197 | "df.drop_duplicates(inplace=True)\n", 198 | "df['Date'] = pd.to_datetime(df['Date'])\n", 199 | "df['Likes'] = df['Likes'].astype(int)" 200 | ] 201 | }, 202 | { 203 | "cell_type": "markdown", 204 | "id": "fe8a453b", 205 | "metadata": {}, 206 | "source": [ 207 | "# Task-5 : Visualize and analyze the data" 208 | ] 209 | }, 210 | { 211 | "cell_type": "code", 212 | "execution_count": 17, 213 | "id": "67da912e", 214 | "metadata": {}, 215 | "outputs": [ 216 | { 217 | "data": { 218 | "image/png": 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", 219 | "text/plain": [ 220 | "
" 221 | ] 222 | }, 223 | "metadata": {}, 224 | "output_type": "display_data" 225 | } 226 | ], 227 | "source": [ 228 | "# Task 5: Visualize and analyze the data\n", 229 | "sns.histplot(df['Likes'])\n", 230 | "plt.title(\"Histogram of likes\")\n", 231 | "plt.show()" 232 | ] 233 | }, 234 | { 235 | "cell_type": "code", 236 | "execution_count": 20, 237 | "id": "6aaa936a", 238 | "metadata": {}, 239 | "outputs": [ 240 | { 241 | "data": { 242 | "image/png": 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", 243 | "text/plain": [ 244 | "
" 245 | ] 246 | }, 247 | "metadata": {}, 248 | "output_type": "display_data" 249 | } 250 | ], 251 | "source": [ 252 | "sns.boxplot(x='Category', y='Likes', data=df)\n", 253 | "plt.title(\"Box plot of Likes by category\")\n", 254 | "plt.show()" 255 | ] 256 | }, 257 | { 258 | "cell_type": "code", 259 | "execution_count": 19, 260 | "id": "efac2335", 261 | "metadata": {}, 262 | "outputs": [ 263 | { 264 | "name": "stdout", 265 | "output_type": "stream", 266 | "text": [ 267 | "5062.91\n", 268 | "Category\n", 269 | "Culture 4872.593220\n", 270 | "Family 5342.959184\n", 271 | "Fashion 5415.352113\n", 272 | "Fitness 5048.000000\n", 273 | "Food 5095.731343\n", 274 | "Health 5046.918605\n", 275 | "Music 4551.627451\n", 276 | "Travel 5039.859375\n", 277 | "Name: Likes, dtype: float64\n" 278 | ] 279 | } 280 | ], 281 | "source": [ 282 | "print(df['Likes'].mean())\n", 283 | "print(df.groupby('Category')['Likes'].mean())" 284 | ] 285 | }, 286 | { 287 | "cell_type": "code", 288 | "execution_count": null, 289 | "id": "048130b8", 290 | "metadata": {}, 291 | "outputs": [], 292 | "source": [] 293 | } 294 | ], 295 | "metadata": { 296 | "kernelspec": { 297 | "display_name": "Python 3 (ipykernel)", 298 | "language": "python", 299 | "name": "python3" 300 | }, 301 | "language_info": { 302 | "codemirror_mode": { 303 | "name": "ipython", 304 | "version": 3 305 | }, 306 | "file_extension": ".py", 307 | "mimetype": "text/x-python", 308 | "name": "python", 309 | "nbconvert_exporter": "python", 310 | "pygments_lexer": "ipython3", 311 | "version": "3.11.5" 312 | } 313 | }, 314 | "nbformat": 4, 315 | "nbformat_minor": 5 316 | } 317 | --------------------------------------------------------------------------------