๐ Data Science Project Ideas to Practice & Master Your Skills โ
๐ข Beginner Level
โข Titanic Survival Prediction (Logistic Regression)
โข House Price Prediction (Linear Regression)
โข Exploratory Data Analysis on IPL or Netflix Dataset
โข Customer Segmentation (K-Means Clustering)
โข Weather Data Visualization
๐ก Intermediate Level
โข Sentiment Analysis on Tweets
โข Credit Card Fraud Detection
โข Time Series Forecasting (Stock or Sales Data)
โข Image Classification using CNN (Fashion MNIST)
โข Recommendation System for Movies/Products
๐ด Advanced Level
โข End-to-End Machine Learning Pipeline with Deployment
โข NLP Chatbot using Transformers
โข Real-Time Dashboard with Streamlit + ML
โข Anomaly Detection in Network Traffic
โข A/B Testing & Business Decision Modeling
๐ฌ Double Tap โค๏ธ for more! ๐ค๐
๐ข Beginner Level
โข Titanic Survival Prediction (Logistic Regression)
โข House Price Prediction (Linear Regression)
โข Exploratory Data Analysis on IPL or Netflix Dataset
โข Customer Segmentation (K-Means Clustering)
โข Weather Data Visualization
๐ก Intermediate Level
โข Sentiment Analysis on Tweets
โข Credit Card Fraud Detection
โข Time Series Forecasting (Stock or Sales Data)
โข Image Classification using CNN (Fashion MNIST)
โข Recommendation System for Movies/Products
๐ด Advanced Level
โข End-to-End Machine Learning Pipeline with Deployment
โข NLP Chatbot using Transformers
โข Real-Time Dashboard with Streamlit + ML
โข Anomaly Detection in Network Traffic
โข A/B Testing & Business Decision Modeling
๐ฌ Double Tap โค๏ธ for more! ๐ค๐
โค6
Guys, Big Announcement!
Weโve officially hit 2.5 Million followers โ and itโs time to level up together! โค๏ธ
Iโm launching a Python Projects Series โ designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step, hands-on journey โ where youโll build useful Python projects with clear code, explanations, and mini-quizzes!
Hereโs what weโll cover:
๐น Week 1: Python Mini Projects (Daily Practice)
โฆ Calculator
โฆ To-Do List (CLI)
โฆ Number Guessing Game
โฆ Unit Converter
โฆ Digital Clock
๐น Week 2: Data Handling & APIs
โฆ Read/Write CSV & Excel files
โฆ JSON parsing
โฆ API Calls using Requests
โฆ Weather App using OpenWeather API
โฆ Currency Converter using Real-time API
๐น Week 3: Automation with Python
โฆ File Organizer Script
โฆ Email Sender
โฆ WhatsApp Automation
โฆ PDF Merger
โฆ Excel Report Generator
๐น Week 4: Data Analysis with Pandas & Matplotlib
โฆ Load & Clean CSV
โฆ Data Aggregation
โฆ Data Visualization
โฆ Trend Analysis
โฆ Dashboard Basics
๐น Week 5: AI & ML Projects (Beginner Friendly)
โฆ Predict House Prices
โฆ Email Spam Classifier
โฆ Sentiment Analysis
โฆ Image Classification (Intro)
โฆ Basic Chatbot
๐ Each project includes:
โ Problem Statement
โ Code with explanation
โ Sample input/output
โ Learning outcome
โ Mini quiz
๐ฌ React โค๏ธ if you're ready to build some projects together!
You can access it for free here
๐๐
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Letโs Build. Letโs Grow. ๐ป๐
Weโve officially hit 2.5 Million followers โ and itโs time to level up together! โค๏ธ
Iโm launching a Python Projects Series โ designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step, hands-on journey โ where youโll build useful Python projects with clear code, explanations, and mini-quizzes!
Hereโs what weโll cover:
๐น Week 1: Python Mini Projects (Daily Practice)
โฆ Calculator
โฆ To-Do List (CLI)
โฆ Number Guessing Game
โฆ Unit Converter
โฆ Digital Clock
๐น Week 2: Data Handling & APIs
โฆ Read/Write CSV & Excel files
โฆ JSON parsing
โฆ API Calls using Requests
โฆ Weather App using OpenWeather API
โฆ Currency Converter using Real-time API
๐น Week 3: Automation with Python
โฆ File Organizer Script
โฆ Email Sender
โฆ WhatsApp Automation
โฆ PDF Merger
โฆ Excel Report Generator
๐น Week 4: Data Analysis with Pandas & Matplotlib
โฆ Load & Clean CSV
โฆ Data Aggregation
โฆ Data Visualization
โฆ Trend Analysis
โฆ Dashboard Basics
๐น Week 5: AI & ML Projects (Beginner Friendly)
โฆ Predict House Prices
โฆ Email Spam Classifier
โฆ Sentiment Analysis
โฆ Image Classification (Intro)
โฆ Basic Chatbot
๐ Each project includes:
โ Problem Statement
โ Code with explanation
โ Sample input/output
โ Learning outcome
โ Mini quiz
๐ฌ React โค๏ธ if you're ready to build some projects together!
You can access it for free here
๐๐
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Letโs Build. Letโs Grow. ๐ป๐
โค13๐1
๐ ๐
๐ซ๐๐ ๐๐๐ ๐๐๐ฌ๐จ๐ฎ๐ซ๐๐๐ฌ ๐ญ๐จ ๐๐ซ๐๐๐ค ๐๐จ๐๐ข๐ง๐ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ๐ฌ๐
Cracking coding interviews isnโt about luckโitโs about mastering Data Structures and Algorithms (DSA) with the right resources๐ฅ๐
Whether youโre aiming for FAANG, top MNCs, or fast-growing startups, having a strong foundation in DSA will set you apart๐งโ๐๐ฅ
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Start today and turn your DSA fear into DSA mastery!โ ๏ธ
Cracking coding interviews isnโt about luckโitโs about mastering Data Structures and Algorithms (DSA) with the right resources๐ฅ๐
Whether youโre aiming for FAANG, top MNCs, or fast-growing startups, having a strong foundation in DSA will set you apart๐งโ๐๐ฅ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/41MsPpe
Start today and turn your DSA fear into DSA mastery!โ ๏ธ
โค1
Which of the following is essential for any well-documented data science project?
Anonymous Quiz
5%
a) Fancy UI design
3%
b) Only code files
83%
c) README file explaining problem, steps & results
10%
d) Just a model accuracy score
โค2
Your model performs well on training data but poorly on test data. Whatโs likely missing?
Anonymous Quiz
25%
a) Hyperparameter tuning
69%
b) Overfitting handling
3%
c) More print statements
3%
d) Fancy visualizations
โค1
Which file should you upload along with your Jupyter Notebook to make your project reproducible?
Anonymous Quiz
6%
a) Screenshot of results
12%
b) Excel output file
76%
c) requirements.txt or environment.yml
6%
d) A video walkthrough
โค1
Which step is often skipped but highly recommended when presenting a project?
Anonymous Quiz
27%
a) Exploratory Data Analysis
36%
b) Writing comments in code
26%
c) Explaining business impact or value
11%
d) Printing all columns of the dataset
โค1
Which of the following is NOT a recommended practice when uploading a data science project to GitHub?*
Anonymous Quiz
20%
A) Including a well-written README.md with setup and usage instructions
66%
B) Uploading large raw datasets directly into the repository
8%
C) Organizing code into modular scripts under a src/ folder
7%
D) Providing a requirements.txt or environment.yml for dependencies
โค1
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Learn Data Analytics, Data Science & AI From Top Data Experts
Curriculum designed and taught by Alumni from IITs & Leading Tech Companies.
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( Hurry Up ๐โโ๏ธLimited Slots )
Learn Data Analytics, Data Science & AI From Top Data Experts
Curriculum designed and taught by Alumni from IITs & Leading Tech Companies.
๐๐ถ๐ด๐ต๐น๐ถ๐ด๐ต๐๐ฒ๐:-
- 12.65 Lakhs Highest Salary
- 500+ Partner Companies
- 100% Job Assistance
- 5.7 LPA Average Salary
๐๐ผ๐ผ๐ธ ๐ฎ ๐๐ฅ๐๐ ๐๐ฒ๐บ๐ผ๐:-
๐ข๐ป๐น๐ถ๐ป๐ฒ :- https://pdlink.in/4fdWxJB
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โค3
๐ ๐ผ๐๐ ๐๐๐ธ๐ฒ๐ฑ ๐ฆ๐ค๐ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐ ๐ฎ๐ ๐ ๐๐๐ก๐ ๐๐ผ๐บ๐ฝ๐ฎ๐ป๐ถ๐ฒ๐๐ฅ๐ฅ
1. How do you retrieve all columns from a table?
SELECT * FROM table_name;
2. What SQL statement is used to filter records?
SELECT * FROM table_name
WHERE condition;
The WHERE clause is used to filter records based on a specified condition.
3. How can you join multiple tables? Describe different types of JOINs.
SELECT columns
FROM table1
JOIN table2 ON table1.column = table2.column
JOIN table3 ON table2.column = table3.column;
Types of JOINs:
1. INNER JOIN: Returns records with matching values in both tables
SELECT * FROM table1
INNER JOIN table2 ON table1.column = table2.column;
2. LEFT JOIN (or LEFT OUTER JOIN): Returns all records from the left table and matched records from the right table. Unmatched records will have NULL values.
SELECT * FROM table1
LEFT JOIN table2 ON table1.column = table2.column;
3. RIGHT JOIN (or RIGHT OUTER JOIN): Returns all records from the right table and matched records from the left table. Unmatched records will have NULL values.
SELECT * FROM table1
RIGHT JOIN table2 ON table1.column = table2.column;
4. FULL JOIN (or FULL OUTER JOIN): Returns records when there is a match in either left or right table. Unmatched records will have NULL values.
SELECT * FROM table1
FULL JOIN table2 ON table1.column = table2.column;
4. What is the difference between WHERE and HAVING clauses?
WHERE: Filters records before any groupings are made.
SELECT * FROM table_name
WHERE condition;
HAVING: Filters records after groupings are made.
SELECT column, COUNT(*)
FROM table_name
GROUP BY column
HAVING COUNT(*) > value;
5. How do you count the number of records in a table?
SELECT COUNT(*) FROM table_name;
This query counts all the records in the specified table.
6. How do you calculate average, sum, minimum, and maximum values in a column?
Average: SELECT AVG(column_name) FROM table_name;
Sum: SELECT SUM(column_name) FROM table_name;
Minimum: SELECT MIN(column_name) FROM table_name;
Maximum: SELECT MAX(column_name) FROM table_name;
7. What is a subquery, and how do you use it?
Subquery: A query nested inside another query
SELECT * FROM table_name
WHERE column_name = (SELECT column_name FROM another_table WHERE condition);
Till then keep learning and keep exploring ๐
1. How do you retrieve all columns from a table?
SELECT * FROM table_name;
2. What SQL statement is used to filter records?
SELECT * FROM table_name
WHERE condition;
The WHERE clause is used to filter records based on a specified condition.
3. How can you join multiple tables? Describe different types of JOINs.
SELECT columns
FROM table1
JOIN table2 ON table1.column = table2.column
JOIN table3 ON table2.column = table3.column;
Types of JOINs:
1. INNER JOIN: Returns records with matching values in both tables
SELECT * FROM table1
INNER JOIN table2 ON table1.column = table2.column;
2. LEFT JOIN (or LEFT OUTER JOIN): Returns all records from the left table and matched records from the right table. Unmatched records will have NULL values.
SELECT * FROM table1
LEFT JOIN table2 ON table1.column = table2.column;
3. RIGHT JOIN (or RIGHT OUTER JOIN): Returns all records from the right table and matched records from the left table. Unmatched records will have NULL values.
SELECT * FROM table1
RIGHT JOIN table2 ON table1.column = table2.column;
4. FULL JOIN (or FULL OUTER JOIN): Returns records when there is a match in either left or right table. Unmatched records will have NULL values.
SELECT * FROM table1
FULL JOIN table2 ON table1.column = table2.column;
4. What is the difference between WHERE and HAVING clauses?
WHERE: Filters records before any groupings are made.
SELECT * FROM table_name
WHERE condition;
HAVING: Filters records after groupings are made.
SELECT column, COUNT(*)
FROM table_name
GROUP BY column
HAVING COUNT(*) > value;
5. How do you count the number of records in a table?
SELECT COUNT(*) FROM table_name;
This query counts all the records in the specified table.
6. How do you calculate average, sum, minimum, and maximum values in a column?
Average: SELECT AVG(column_name) FROM table_name;
Sum: SELECT SUM(column_name) FROM table_name;
Minimum: SELECT MIN(column_name) FROM table_name;
Maximum: SELECT MAX(column_name) FROM table_name;
7. What is a subquery, and how do you use it?
Subquery: A query nested inside another query
SELECT * FROM table_name
WHERE column_name = (SELECT column_name FROM another_table WHERE condition);
Till then keep learning and keep exploring ๐
โค5๐2