Python Projects & Free Books

𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀 𝗳𝗿𝗼𝗺 𝗚𝗹𝗼𝗯𝗮𝗹 𝗚𝗶𝗮𝗻𝘁𝘀!😍 Want real-world experience in 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆, 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲, 𝗼𝗿 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜? 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4hZlkAW 🔗 Save & share this post with someone who needs it!

𝗚𝗲𝘁 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗿𝗼𝗺 𝗛𝗮𝗿𝘃𝗮𝗿𝗱, 𝗠𝗜𝗧 & 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱 – 𝗡𝗼 𝗖𝗼𝘀𝘁!😍 Why spend thousands on courses when the world’s top universities offer them for FREE? 🤯 This website gives you unlimited access to high-quality courses from: ✅ 𝗛𝗮𝗿𝘃𝗮𝗿𝗱 ✅ 𝗠𝗜𝗧 ✅ 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱 ✅ 𝗬𝗮𝗹𝗲 & 𝗠𝗼𝗿𝗲! 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4aY7jBi 📌 Save this & tag a friend who needs to see this! 🚀

𝗟𝗲𝗮𝗿𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘😍 Want to master Python and level up your data analytics skills?✨️ These high-quality tutorials to help you go from beginner to pro!✅️ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4hXQOHQ 📢 No cost, no catch – just pure learning! 🚀

𝗙𝗥𝗘𝗘 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 1)Business Analysis – Foundation 2)Business Analysis Fundamentals 3)The Essentials of Business & Risk Analysis  4)Master Microsoft Power BI  𝗟𝗶𝗻𝗸 👇:- https://pdlink.in/4hHxBdW Enroll For FREE & Get Certified🎓

Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself. 1. Basic python and statistics Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness Automobile :- https://www.kaggle.com/toramky/automobile-dataset 2. Advanced Statistics Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset 3. Supervised Learning a) Regression Problems How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview b) Classification problems Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview Titanic :- https://www.kaggle.com/c/titanic San Francisco crime:- https://www.kaggle.com/c/sf-crime Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification Categorize cusine:- https://www.kaggle.com/c/whats-cooking 4. Some helpful Data science projects for beginners https://www.kaggle.com/c/house-prices-advanced-regression-techniques https://www.kaggle.com/c/digit-recognizer https://www.kaggle.com/c/titanic 5. Intermediate Level Data science Projects Black Friday Data : https://www.kaggle.com/sdolezel/black-friday Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset Million Song Data : https://www.kaggle.com/c/msdchallenge Census Income Data : https://www.kaggle.com/c/census-income/data Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2 Share with credits: https://t.me/sqlproject ENJOY LEARNING 👍👍

𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 & 𝗨𝗻𝗹𝗼𝗰𝗸 𝗛𝗶𝗴𝗵-𝗣𝗮𝘆𝗶𝗻𝗴 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀!😍 Top 3 Free YouTube Playlists to Learn SQL 1)SQL Tutorial Videos 2)SQL Mastery: From Basics to Advanced 3)Learn Complete SQL (Beginner to Advanced) 𝗟𝗶𝗻𝗸 👇:- https://pdlink.in/4hFyseX Enroll For FREE & Get Certified🎓

Python project-based interview questions for a data analyst role, along with tips and sample answers [Part-1] 1. Data Cleaning and Preprocessing    - Question: Can you walk me through the data cleaning process you followed in a Python-based project?    - Answer: In my project, I used Pandas for data manipulation. First, I handled missing values by imputing them with the median for numerical columns and the most frequent value for categorical columns using fillna(). I also removed outliers by setting a threshold based on the interquartile range (IQR). Additionally, I standardized numerical columns using StandardScaler from Scikit-learn and performed one-hot encoding for categorical variables using Pandas' get_dummies() function.    - Tip: Mention specific functions you used, like dropna(), fillna(), apply(), or replace(), and explain your rationale for selecting each method. 2. Exploratory Data Analysis (EDA)    - Question: How did you perform EDA in a Python project? What tools did you use?    - Answer: I used Pandas for data exploration, generating summary statistics with describe() and checking for correlations with corr(). For visualization, I used Matplotlib and Seaborn to create histograms, scatter plots, and box plots. For instance, I used sns.pairplot() to visually assess relationships between numerical features, which helped me detect potential multicollinearity. Additionally, I applied pivot tables to analyze key metrics by different categorical variables.    - Tip: Focus on how you used visualization tools like Matplotlib, Seaborn, or Plotly, and mention any specific insights you gained from EDA (e.g., data distributions, relationships, outliers). 3. Pandas Operations    - Question: Can you explain a situation where you had to manipulate a large dataset in Python using Pandas?    - Answer: In a project, I worked with a dataset containing over a million rows. I optimized my operations by using vectorized operations instead of Python loops. For example, I used apply() with a lambda function to transform a column, and groupby() to aggregate data by multiple dimensions efficiently. I also leveraged merge() to join datasets on common keys.    - Tip: Emphasize your understanding of efficient data manipulation with Pandas, mentioning functions like groupby(), merge(), concat(), or pivot(). 4. Data Visualization    - Question: How do you create visualizations in Python to communicate insights from data?    - Answer: I primarily use Matplotlib and Seaborn for static plots and Plotly for interactive dashboards. For example, in one project, I used sns.heatmap() to visualize the correlation matrix and sns.barplot() for comparing categorical data. For time-series data, I used Matplotlib to create line plots that displayed trends over time. When presenting the results, I tailored visualizations to the audience, ensuring clarity and simplicity.    - Tip: Mention the specific plots you created and how you customized them (e.g., adding labels, titles, adjusting axis scales). Highlight the importance of clear communication through visualization.

𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗧𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿😍 1) Introduction to Cyber Security 2) AWS Cloud Masterclass 3)Salesforce Developer Catalyst 4) Python Basics 5) Project Management Basics 𝗟𝗶𝗻𝗸 👇:- https://pdlink.in/4jQJfo5 Enroll For FREE & Get Certified🎓

𝗟𝗲𝗮𝗿𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 𝘄𝗶𝘁𝗵 𝗚𝗼𝗼𝗴𝗹𝗲’𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀!😍 You want to break into IT automation, data analysis, or software development✨️ These FREE Google-backed courses will help you master Python from scratch!💡 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/42QHRM5 📢 Don’t miss out! Invest in your future and start learning today! 🚀

Use Python to turn messy data into valuable insights! Here are the main functions you need to know: 1. 𝗱𝗿𝗼𝗽𝗻𝗮(): Clean up your dataset by removing missing values. Use df.dropna() to eliminate rows or columns with NaNs and keep your data clean.     2. 𝗳𝗶𝗹𝗹𝗻𝗮(): Replace missing values with a specified value or method. With the help of df.fillna(value) you maintain data integrity without losing valuable information.     3. 𝗱𝗿𝗼𝗽_𝗱𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝘀(): Ensure your data is unique and accurate. Use df.drop_duplicates() to remove duplicate rows and avoid skewing your analysis by aggregating redundant data.     4. 𝗿𝗲𝗽𝗹𝗮𝗰𝗲(): Substitute specific values throughout your dataset. The function df.replace(to_replace, value) allows for efficient correction of errors and standardization of data.     5. 𝗮𝘀𝘁𝘆𝗽𝗲(): Convert data types for consistency and accuracy. Use the cast function df['column'].astype(dtype) to ensure your data columns are in the correct format you need for your analysis.     6. 𝗮𝗽𝗽𝗹𝘆(): Apply custom functions to your data. df['column'].apply(func) lets you perform complex transformations and calculations. It works with both standard and lambda functions.     7. 𝘀𝘁𝗿.𝘀𝘁𝗿𝗶𝗽(): Clean up text data by removing leading and trailing whitespace. Using df['column'].str.strip() helps you to avoid hard-to-spot errors in string comparisons.     8. 𝘃𝗮𝗹𝘂𝗲_𝗰𝗼𝘂𝗻𝘁𝘀(): Get a quick summary of the frequency of values in a column. df['column'].value_counts() helps you understand the distribution of your data.     9. 𝗽𝗱.𝘁𝗼_𝗱𝗮𝘁𝗲𝘁𝗶𝗺𝗲(): Convert strings to datetime objects for accurate date and time manipulation. For time series analysis the use of pd.to_datetime(df['column']) will often be one of your first steps in data preparation.     10. 𝗴𝗿𝗼𝘂𝗽𝗯𝘆(): Aggregates data based on specific columns. Use df.groupby('column') to perform operations like sum, mean, or count on grouped data. Learn to use these Python functions, to be able to transform a pile of messy data into the starting point of an impactful analysis.

𝗙𝗿𝗲𝗲 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗕𝘆 𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀😍 - JP Morgan  - Accenture - Walmart - Tata Group - Accenture 𝗟𝗶𝗻𝗸 👇:- https://pdlink.in/3WTGGI8 Enroll For FREE & Get Certified🎓

MIT's "Machine Learning" lecture notes PDF: https://introml.mit.edu/_static/spring24/LectureNotes/6_390_lecture_notes_spring24.pdf

𝗬𝗼𝘂𝗿 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘁𝗼 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁!😍 Want to break into Data Analytics but don’t know where to start? Follow this step-by-step roadmap to build real-world skills! ✅ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3CHqZg7 🎯 Start today & build a strong career in Data Analytics! 🚀

𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗜𝗻 𝟮𝟬𝟮𝟱😍 Master industry-standard tools like Excel, SQL, Tableau, and more. Gain hands-on experience through real-world projects designed to mimic professional challenges 𝗟𝗶𝗻𝗸👇 :-  https://pdlink.in/4jxUW2K All The Best 🎉

15 Best Project Ideas for Python : 🐍 🚀 Beginner Level: 1. Simple Calculator 2. To-Do List 3. Number Guessing Game 4. Dice Rolling Simulator 5. Word Counter 🌟 Intermediate Level: 6. Weather App 7. URL Shortener 8. Movie Recommender System 9. Chatbot 10. Image Caption Generator 🌌 Advanced Level: 11. Stock Market Analysis 12. Autonomous Drone Control 13. Music Genre Classification 14. Real-Time Object Detection 15. Natural Language Processing (NLP) Sentiment Analysis Here you can find essential Python Resources👇 https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L Like this post for more resources like this 👍♥️

𝗠𝗮𝘀𝘁𝗲𝗿 𝗧𝗼𝗽 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘😍 Best Free Platforms to Learn Top Technologies - Cybersecurity - Data Analytics - Machine Learning - DevOps    𝗟𝗲𝗮𝗿𝗻 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:- https://pdlink.in/42NiQSa Enroll For FREE & Get Certified

𝗦𝗤𝗟 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 Best Free SQL Courses to Get Started 1) Introduction to Databases and SQL 2) Advanced Database and SQL 3) Learn SQL  4) SQL Tutorial 𝐋𝐢𝐧𝐤 👇:-  https://pdlink.in/3EyjUPt Enroll For FREE & Get Certified 🎓

Pie Chart Using Pandas