Data Science Projects with Source Code

Quick answer

The best data science projects use real public data, answer a clear question and end with a model or dashboard you can explain. Start with beginner projects such as Titanic survival prediction, house price prediction and an IPL or COVID-19 data analysis. Then move to intermediate machine learning (credit card fraud detection, customer segmentation, sales forecasting), NLP and computer vision projects, and finish with one advanced, deployed project such as a RAG chatbot or an end-to-end ML app. This guide lists 70 project ideas by level and domain, with datasets, skills, Python code samples and GitHub source code links we have checked.

Key takeaways

1. What makes a good data science project

Short answer: A good data science project answers one clear question with real data, shows clean exploratory analysis, builds a model you can evaluate honestly, and explains the result in plain words. Deployment, such as a small Streamlit app, is a strong bonus. Pick a project that matches your current level, then push one step beyond it.

Many students build ten copies of the same tutorial and wonder why recruiters do not call. The problem is not the number of projects. It is that the projects do not show thinking. A recruiter or hiring manager spends a few minutes on your GitHub. In that time they want to see that you can take messy data, ask a sensible question and reach a useful answer.

What recruiters look for in your projects

Here is what an interviewer in India usually checks when they open one of your project repositories:

If you are not sure which of these skills you still need to learn, our guide to data science skills lists them in the order most freshers pick them up.

How to pick a project for your level

Choose by what you can already do, not by what sounds impressive. A clean, well-explained beginner project beats a half-finished deep learning project every time.

A good portfolio for a fresher has three to five projects: one or two beginner EDA projects, two intermediate machine learning projects, and one project that is deployed or uses a newer tool such as Hugging Face Transformers.

Mistakes that make a project look weak

Avoid these common problems. Each one is easy to fix.

2. Tools and setup you need

Short answer: You need Python 3, a notebook environment (Jupyter or Google Colab), the core libraries pandas, NumPy, Matplotlib, Seaborn and scikit-learn, and a GitHub account to share your work. For data, use Kaggle, the UCI Machine Learning Repository, data.gov.in and Google Dataset Search. All of these are free.

You do not need a powerful laptop for the projects in this guide. Every beginner and intermediate project runs on an ordinary laptop with 8 GB of RAM. When you reach deep learning, Google Colab and Kaggle Notebooks give you free GPU time in the browser.

Core tools for data science projects

Later projects use TensorFlow/Keras or PyTorch for deep learning, Hugging Face Transformers for NLP, Prophet or statsmodels for time series, and Power BI or Tableau for dashboards. You do not need to learn them all at once. Our data science tool list explains what each tool does and when you need it.

A simple setup that works

Follow these steps once and you are ready for every project in this post:

Where to find datasets

Good data makes a good project. These four sources cover almost everything you need:

You can browse all public Kaggle datasets by topic and filter by file type and size. For India-focused projects, data.gov.in is worth extra time. A project on Indian crop production, road accidents or state-wise literacy shows interviewers that you can work with local data, not only the usual Western examples.

3. Beginner data science projects (EDA and basic ML)

Short answer: Start with small, clean datasets where you can practise loading data, handling missing values, drawing charts and training a first model. Titanic, Iris, house prices, student performance and sports or entertainment datasets like IPL and Netflix are ideal. Finish two or three of these properly before moving on.

These projects teach the workflow you will reuse for every bigger project: question, data, cleaning, EDA, model, evaluation and conclusion. Most of them take a weekend or two. For each one, try to add one thing that the common tutorials do not have.

1. Titanic survival prediction

Level: Beginner | Skills: pandas, missing values, encoding, classification | Dataset: Kaggle: Titanic – Machine Learning from Disaster

You predict whether a passenger survived the Titanic sinking from details like ticket class, sex, age, family size and fare. It is the classic first project because the dataset is small, the question is clear and every step of the workflow appears in it. You learn to fill missing ages, encode text columns, build a baseline and compare models such as logistic regression and random forest.

Here is a short baseline you can run in Jupyter or Colab once you have the train.csv file:

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

df = pd.read_csv("train.csv") # Kaggle Titanic training file
df["Sex"] = df["Sex"].map({"male": 0, "female": 1})
df["Age"] = df["Age"].fillna(df["Age"].median())
X = df[["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare"]]
y = df["Survived"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = RandomForestClassifier(n_estimators=200, random_state=42)
model.fit(X_train, y_train)
print("Accuracy:", round(model.score(X_test, y_test), 3))

To make it your own, add the Title feature, try cross-validation instead of one split, and submit predictions to the Kaggle leaderboard.

Source code: agconti/kaggle-titanic

2. Iris flower classification

Level: Beginner | Skills: EDA, multi-class classification, cross-validation | Dataset: UCI Iris (also built into scikit-learn)

You classify iris flowers into three species using four measurements: sepal length, sepal width, petal length and petal width. The dataset has only 150 rows and no missing values, so you can focus fully on understanding models. You learn pair plots, train-test splits and how k-nearest neighbours, decision trees and support vector machines compare on the same data.

The dataset loads with one line, load_iris(), from sklearn.datasets, so there is nothing to download. Because almost everyone has an Iris project, add something extra: plot the decision boundaries of two models side by side, or explain in plain words why petal measurements separate the species better than sepal measurements.

3. House price prediction

Level: Beginner | Skills: regression, feature engineering, RMSE | Dataset: Kaggle: House Prices – Advanced Regression Techniques (Ames, Iowa)

You predict the sale price of a house from about 79 features such as lot size, year built, number of rooms, quality ratings and neighbourhood. This is your first regression project with real messiness: many missing values, skewed prices and lots of categorical columns. You learn log-transforming the target, one-hot encoding, and comparing linear regression, random forest and gradient boosting using RMSE.

A good variation for Indian learners is predicting rental prices from listing data, or Airbnb listing prices using the Airbnb Open Data dataset on Kaggle. The steps are the same; only the features change.

Source code: sinhasagar507/Ames-house-price-prediction

4. Student performance prediction

Level: Beginner | Skills: regression, correlation analysis, feature selection | Dataset: UCI Student Performance

You predict a student's final grade from earlier grades, study time, absences, family background and other habits. The dataset comes from two Portuguese secondary schools and has math and Portuguese language versions. You learn correlation heatmaps, how strongly earlier grades predict the final grade, and how results change when you remove those grades and use only lifestyle features.

That last step is what makes this project interesting. With earlier grades included, almost any model looks good. Without them, you find out which habits really matter. Write up both versions and explain the difference; it shows interviewers you understand what a model is actually learning.

Source code: William-Laverty/ML-Student-Performance-Predictor

5. Weather data analysis

Level: Beginner | Skills: time-based EDA, resampling, line charts | Dataset: Kaggle: Weather Dataset (hourly readings)

You explore a year or more of hourly weather readings to find temperature patterns, seasonal changes, humidity and wind trends, and unusual days. This project teaches you to work with dates in pandas: converting text to datetime, extracting month and hour, and resampling hourly data into daily or monthly averages. It is a gentle introduction to the time series ideas you use later in forecasting.

You can start with this weather dataset on Kaggle. For an India version, look for city-wise temperature and rainfall data from Indian sources and compare, for example, monsoon rainfall in Hyderabad, Mumbai and Chennai. As a small ML extension, classify the weather condition (sunny, rainy, cloudy) from temperature, humidity and pressure.

Source code: Hrushikraj/Data_Analysis_on_Weather_Dataset

6. COVID-19 data tracker

Level: Beginner | Skills: data merging, aggregation, time series charts, Plotly | Dataset: Johns Hopkins CSSE COVID-19 data

You build a tracker that shows confirmed cases, deaths and daily new cases over time for India and other countries. The real lesson here is data wrangling: the raw files are wide (one column per date), so you reshape them to a long format with pandas melt, compute daily changes and 7-day rolling averages, and compare countries fairly using per-million numbers.

The Johns Hopkins CSSE COVID-19 repository holds the historical daily data. It stopped updating in March 2023, so treat this as a historical analysis. For a stronger project, add an interactive Plotly chart where the user picks countries, and a short section that compares the first and second waves in India.

7. Netflix content analysis

Level: Beginner | Skills: text columns, date parsing, grouping, storytelling | Dataset: Kaggle: Netflix Movies and TV Shows

You analyse the Netflix catalogue to find out how the mix of movies and TV shows has changed, which countries produce the most content, which genres dominate, and how long after release titles get added. You learn to split comma-separated columns (genres, cast, countries), parse dates and turn simple counts into a clear story.

Use the Netflix Movies and TV Shows dataset. Add an India angle: how much Indian content is on the platform, which genres it falls into, and how that changed by year. If you want a small ML step, build a simple content-based recommender using genres and descriptions.

8. IPL data analysis

Level: Beginner | Skills: joins, groupby, sports metrics, dashboards | Dataset: Kaggle: IPL Complete Dataset (matches and ball-by-ball deliveries)

You analyse IPL seasons to find top run-scorers and wicket-takers, strike rates, team win ratios and whether winning the toss really helps. The dataset has two tables, matches and deliveries, so you learn to join them and calculate cricket metrics yourself. Indian interviewers relate to this project instantly, which makes it a good conversation starter.

As an extension, train a simple model that predicts the match winner from toss result, venue and team strength, and explain why its accuracy is limited.

9. Google Play Store app analysis

Level: Beginner | Skills: data cleaning, text features, basic sentiment | Dataset: Kaggle: Google Play Store Apps (apps and user reviews)

You explore thousands of Play Store apps to see which categories have the highest ratings, how price and size relate to installs, and what users say in reviews. The raw data is messy on purpose: installs like "10,000+", sizes in "M" and "k", and duplicate rows. Cleaning it teaches you more real pandas than a tidy dataset ever will.

Get the Google Play Store Apps dataset, which also includes a reviews file with sentiment labels. Add a basic sentiment model using TF-IDF and logistic regression on the reviews. If you want to practise collecting data, you can also scrape review text for a few Indian apps with a Python library and compare categories such as fintech, food delivery and edtech.

10. Olympics data analysis

Level: Beginner | Skills: EDA, grouping, trend analysis | Dataset: Kaggle: 120 Years of Olympic History – Athletes and Results

You explore more than a century of Olympic results to find which countries win the most medals, how women's participation has grown, which sports each country dominates and how athlete age and height differ by sport. You learn to count medals correctly (a team event gives one medal to the country, not one per player), and to present long-term trends clearly.

Add an India section: India's medal history by sport, and how its participation has changed across Games. A nice extra is a chart of medals per million people, which changes the ranking a lot and gives you a strong talking point.

11. FIFA World Cup data exploration

Level: Beginner | Skills: EDA, aggregation, line and bar charts | Dataset: Kaggle: FIFA World Cup (matches, players, cups)

You study FIFA World Cup history: goals per match over time, the most successful teams, home advantage for host nations and how often matches go to extra time. The data comes in separate files for tournaments, matches and players, so you practise joins and summary tables. You also learn to clean team names that changed over time, such as West Germany and Germany.

Start with the FIFA World Cup dataset. Pick one question and answer it well, for example "Has the World Cup become lower scoring?" Then back it up with a chart and a simple statistical test.

12. Spam email and SMS detection

Level: Beginner | Skills: text cleaning, TF-IDF, Naive Bayes, precision and recall | Dataset: Kaggle: SMS Spam Collection

You build a classifier that labels a message as spam or not spam. It is the friendliest introduction to NLP: you clean text, convert words into numbers with CountVectorizer or TF-IDF, and train a Multinomial Naive Bayes model. You also learn why precision matters here, because marking a real message as spam is worse than letting one spam message through.

Source code: GauravG-20/Spam-Email-Detection-using-MultinomialNB

13. Diabetes prediction

Level: Beginner | Skills: classification, scaling, handling hidden missing values | Dataset: Kaggle/UCI: Pima Indians Diabetes Database

You predict whether a patient has diabetes from measurements such as glucose, BMI, blood pressure, insulin and age. The dataset looks clean, but several columns use 0 where the value is actually missing, for example a blood pressure of 0. Spotting and fixing this teaches you to question data instead of trusting it. You compare logistic regression, random forest and XGBoost, and focus on recall, because missing a diabetic patient is costly.

Be careful with claims: this is a learning project on a small dataset, not a medical tool, and saying so clearly shows maturity.

14. Wine quality prediction

Level: Beginner | Skills: regression vs classification, feature importance, class imbalance | Dataset: UCI Wine Quality (red and white)

You predict wine quality scores from chemical properties such as acidity, sugar, sulphates and alcohol. The interesting choice is how to frame the problem: as regression on the score, or as classification into "good" and "not good". Trying both teaches you how framing changes the metric and the result. Most quality scores sit in the middle, so you also meet class imbalance for the first time.

Finish with a feature importance chart and a one-paragraph explanation of which properties drive quality. A similar beginner project is breast cancer classification with the Wisconsin dataset, which is also built into scikit-learn.

4. Data visualization and dashboard projects

Short answer: Dashboard projects show that you can turn data into decisions for non-technical people. Build at least one in Power BI or Tableau and one in Python with Streamlit. Use Indian datasets where you can, such as startup funding or census data, because they make your portfolio memorable.

Many data science and data analyst roles in India ask for Power BI or Tableau alongside Python. A dashboard project proves you can design KPIs, choose the right chart and build filters that help someone answer their own questions. Keep each dashboard to one page with a clear title, three to five KPIs at the top and charts that read left to right.

15. Sales performance dashboard in Power BI or Tableau

Level: Beginner | Skills: Power BI or Tableau, KPIs, DAX or calculated fields, filters | Dataset: Sample Superstore sales data (Tableau and Kaggle)

You build an interactive sales dashboard that shows total sales, profit, profit margin and order count, with filters for region, category and date. You learn to design KPIs, write measures (DAX in Power BI or calculated fields in Tableau), add a map for state-wise sales and highlight loss-making products. This is the dashboard hiring managers most often ask about in analyst interviews.

Publish the Tableau version on Tableau Public or share Power BI screenshots and the .pbix file in your GitHub repository.

16. Indian startup funding analysis

Level: Beginner | Skills: data cleaning, currency handling, trend and city analysis | Dataset: Kaggle: Indian Startup Funding

You analyse funding rounds for Indian startups to see how much money was raised each year, which sectors and cities attract the most investment, who the most active investors are and how round sizes have changed. The data has inconsistent city names (Bangalore and Bengaluru), messy amounts with commas and text, and mixed sector labels. Cleaning it carefully is half the project.

Use the Indian Startup Funding dataset. Present the final result as a dashboard with a year slider, a city map and a sector breakdown. A strong extra is to tag each startup's sector into a smaller set of groups (fintech, edtech, health, e-commerce) and compare their funding curves.

17. YouTube trending video analysis

Level: Beginner to intermediate | Skills: EDA, engagement metrics, text analysis of titles and tags | Dataset: Kaggle: YouTube Trending Video Dataset (includes India)

You study trending YouTube videos to find what they have in common: categories, title length, upload hour, tags, likes-to-views ratio and how long videos stay on the trending list. The dataset has separate files for many countries, including India, so you can compare what trends in India against other countries.

Start with the YouTube Trending Video Dataset. Map the numeric category IDs to names using the JSON file included with the data. Add a word cloud of common title words, and finish with practical advice a content creator could actually use.

18. Indian census visualization

Level: Beginner to intermediate | Skills: maps, geographic data, ratios and per-capita metrics | Dataset: Census of India 2011 data (data.gov.in)

You visualise state-wise and district-wise population, literacy rate, sex ratio and urban versus rural share. You learn to work with geographic data, join tables to state or district shapes, and build choropleth maps in Power BI, Tableau or Python with Plotly or GeoPandas. You also learn to choose fair measures, such as literacy rate instead of the raw number of literate people.

Census 2011 is the latest full census data publicly available, so state that in your project. Pick two or three indicators and tell a story, for example how literacy rate and sex ratio vary across southern and northern states. Keep your district names consistent, because spellings often differ between files.

19. Global terrorism data analysis

Level: Intermediate | Skills: large-dataset EDA, geospatial charts, time trends | Dataset: Kaggle: Global Terrorism Database (GTD)

You explore a large database of recorded incidents to find which regions are most affected, how attack types and targets have changed over decades, and how casualties vary by region. The dataset has over a hundred columns, so you learn to select what matters, handle many missing values and build clear maps and timelines.

Use the Global Terrorism Database on Kaggle. Keep the tone factual and narrow your scope, for example South Asia only, so the analysis stays deep rather than wide.

20. Interactive data dashboard with Streamlit

Level: Beginner to intermediate | Skills: Streamlit, Plotly, file upload, app deployment | Dataset: Any CSV, for example your own sales or IPL data

You build a web app where a user uploads a CSV file, sees a preview, picks columns and gets charts and summary statistics instantly. This is the easiest way to move from notebooks to a real app, and you will reuse the same skills to deploy machine learning models later. You learn Streamlit widgets (file uploader, select box, slider), caching and how to deploy for free on Streamlit Community Cloud.

Here is a tiny starting point. Save it as app.py and run "streamlit run app.py" in your terminal:

import pandas as pd
import streamlit as st

st.title("CSV Explorer")
file = st.file_uploader("Upload a CSV file", type="csv")
if file:
    df = pd.read_csv(file)
    st.write(df.shape[0], "rows and", df.shape[1], "columns")
    st.dataframe(df.head())
    col = st.selectbox("Pick a numeric column", df.select_dtypes("number").columns)
    st.line_chart(df[col])

Grow it step by step: add filters, a correlation heatmap, a missing-values report and a download button for the cleaned data.

Source code: Akshar106/Data-Analysis-and-Visualization-WebApp

5. Intermediate machine learning projects

Short answer: Intermediate projects show that you can handle messy, imbalanced or time-based data and choose the right model and metric. Build at least one supervised project (like loan default or fraud detection), one unsupervised project (like customer segmentation) and one time series forecast. These are the projects interviewers ask about in the most detail.

At this level, the model matters less than your decisions. Why did you pick recall over accuracy? How did you handle class imbalance? Why did you choose five clusters? Write these reasons in your notebook and README. If you want a structured path through these topics, our data science course syllabus shows how supervised, unsupervised and time series modules build on each other.

Supervised learning projects

21. Loan default and loan approval prediction

Level: Intermediate | Skills: classification, class imbalance, threshold tuning, deployment | Dataset: Kaggle loan default data (about 252,000 applicants)

You predict whether a borrower will fail to repay a loan using income, loan amount, credit history, employment details and similar features. Banks and NBFCs in India use this kind of model every day, which makes it highly relevant for finance interviews. You learn to deal with imbalanced classes, tune the decision threshold to balance missed defaults against rejected good customers, and explain the model to a non-technical audience.

For a lighter version, try loan approval prediction with the smaller Loan Prediction dataset, which has only a few hundred applicants and is popular with beginners.

Source code: JensBender/loan-default-prediction

22. Credit card fraud detection

Level: Intermediate | Skills: extreme class imbalance, anomaly detection, precision-recall | Dataset: Kaggle: Credit Card Fraud Detection (European cardholders)

You detect fraudulent card transactions in a dataset of 284,807 transactions where only 492 are fraud, less than 0.2%. This project teaches the most important lesson in applied machine learning: accuracy can lie. You compare supervised models with resampling or class weights against unsupervised methods like Isolation Forest and Local Outlier Factor, and judge them with precision, recall and the precision-recall curve.

With UPI and card payments growing fast in India, you can open your README with why real-time fraud detection matters, without inventing numbers.

Source code: Pradnya1208/Credit-card-fraud-detection-using-Isolation-Forest-and-LOF

23. Heart disease prediction

Level: Intermediate | Skills: classification, model comparison, interpretability | Dataset: UCI Heart Disease (Kaggle version)

You predict the presence of heart disease from clinical features such as age, chest pain type, resting blood pressure, cholesterol and maximum heart rate. You compare decision tree, logistic regression, random forest and XGBoost, then explain the best model with feature importance or SHAP values. Because the dataset is small, you also learn why cross-validation gives a more reliable score than one train-test split.

Get the data from this Heart Disease dataset on Kaggle. Like the diabetes project, state clearly that it is a learning model. Add a simple Streamlit form where a user enters values and sees the predicted risk with the top contributing features.

Source code: surdebmalya/Heart-Disease-UCI

24. Employee attrition prediction

Level: Intermediate | Skills: classification, statistical tests, imbalanced data, HR insights | Dataset: Kaggle: IBM HR Analytics Employee Attrition and Performance

You predict which employees are likely to leave a company using features such as age, job role, monthly income, overtime, job satisfaction and years at the company. The dataset has 1,470 employees, and only a small share left, so imbalance matters again. You learn to use chi-square and ANOVA tests to check which factors relate to attrition, then compare several classifiers.

Use the IBM HR Analytics Attrition dataset. The best part of this project is the recommendations section: turn your findings into three or four actions an HR team could take, such as reviewing overtime policies for certain roles. Note that this is a fictional dataset created by IBM data scientists, so present it as a case study.

Source code: shantanu1109/IBM-HR-Analytics-Employee-Attrition-and-Performance-Prediction

25. Customer churn prediction

Level: Intermediate | Skills: classification, feature engineering, business metrics | Dataset: Kaggle: Telco Customer Churn (IBM sample data)

You predict which telecom customers will cancel their service using contract type, tenure, monthly charges, payment method and services used. Churn is one of the most common business problems in India, from telecom and OTT to banking and SaaS. You learn to engineer features like average monthly spend and tenure bands, train and compare models, and estimate the revenue you could save by targeting likely churners.

Go beyond the model by building a simple "retention list": rank customers by churn probability and show what share of churners you catch if you contact the top 20%. This kind of lift chart is exactly how business teams think, and it makes your project feel real.

26. Credit risk scoring

Level: Intermediate | Skills: probability models, weight of evidence, scorecards, explainability | Dataset: Kaggle: Credit Risk Dataset, or LendingClub loan data

You build a model that gives each loan applicant a probability of default and turns it into a simple risk score or grade. Unlike project 21, the focus here is on using only information available at the time of the loan, calibrating probabilities and explaining the score. Lenders need models they can justify to customers and regulators, so a clear logistic regression often beats a black box.

Source code: tysonpond/lending-club-risk

Unsupervised learning projects

27. Customer segmentation with K-Means

Level: Intermediate | Skills: clustering, scaling, elbow method, cluster profiling | Dataset: Kaggle: Mall Customer Segmentation Data

You group shopping mall customers into segments based on annual income and spending score, so a marketing team can target each group differently. There is no "correct answer" column, which teaches you a new way of thinking: you judge clusters by how useful and distinct they are. You learn scaling, the elbow method, silhouette scores and how to name and describe each segment in business terms.

This short script scales the data, prints the inertia for each k and then profiles five segments:

import pandas as pd
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

df = pd.read_csv("Mall_Customers.csv")
cols = ["Annual Income (k$)", "Spending Score (1-100)"]
X = StandardScaler().fit_transform(df[cols])
for k in range(2, 9):
    km = KMeans(n_clusters=k, n_init=10, random_state=42).fit(X)
    print(k, round(km.inertia_, 1))
df["Segment"] = KMeans(n_clusters=5, n_init=10, random_state=42).fit_predict(X)
print(df.groupby("Segment")[cols].mean().round(1))

For a stronger version, use an e-commerce transactions dataset and build RFM features (recency, frequency, monetary value) before clustering. That is how segmentation is done in real retail and e-commerce teams.

Source code: NelakurthiSudheer/Mall-Customers-Segmentation

28. Market basket analysis

Level: Intermediate | Skills: association rules, Apriori, support, confidence, lift | Dataset: Kaggle: Groceries Dataset, or Instacart Market Basket Analysis

You find which products customers tend to buy together, like bread and butter, so a store can plan shelf layout, combo offers and "frequently bought together" suggestions. You learn to convert transaction data into a basket format, run the Apriori or FP-Growth algorithm with the mlxtend library in Python, and read support, confidence and lift correctly.

Start with the smaller Groceries Dataset, then try the much larger Instacart Market Basket Analysis data once your code works. Present the top ten rules in a table and explain what a lift above 1 means in plain words.

29. Crime pattern clustering across Indian states

Level: Intermediate | Skills: clustering, normalisation, PCA, maps | Dataset: State-wise crime statistics (NCRB data via data.gov.in or Kaggle)

You group Indian states and union territories by the pattern of reported crimes, such as theft, assault, cybercrime and crimes against women. You learn why you must convert raw counts into rates per lakh population before clustering, how PCA helps you plot many features in two dimensions, and how to compare K-Means with hierarchical clustering.

Show the final clusters on a map of India and describe each group carefully. Reported crime numbers depend on how willing people are to report and how police record cases, so add a short limitations section. That kind of honesty impresses interviewers far more than bold conclusions.

Time series forecasting projects

30. Stock price prediction

Level: Intermediate | Skills: time series features, lag variables, XGBoost, walk-forward validation | Dataset: NSE stock market historical data (Kaggle), or prices downloaded with yfinance

You forecast the next day's closing price of a stock using past prices, moving averages and technical indicators such as RSI. The most valuable lesson is not the model but the evaluation: you must never shuffle time series data, and you should compare your model against a naive "tomorrow equals today" baseline. Many tutorials skip this and report results that would not hold up in reality.

Keep the conclusion honest: this is a forecasting exercise, not trading advice. Later you can try an LSTM in TensorFlow/Keras or PyTorch and compare it with XGBoost.

Source code: lluissalvat/XGBoost-Stock-Price-Prediction

31. Electricity demand forecasting

Level: Intermediate | Skills: seasonality, weather features, LSTM or gradient boosting | Dataset: Hourly electricity demand data (for example the NYISO dataset on Kaggle)

You forecast hourly or daily electricity demand using past demand, time of day, day of week, holidays and temperature. Demand has strong daily, weekly and yearly patterns, so you learn to decompose a time series, build calendar and weather features, and compare a classical model with gradient boosting or an LSTM. Power companies use these forecasts to plan supply and avoid shortages.

Try the NYISO hourly demand toolkit data, or look for Indian state load data published by grid operators. Add temperature as a feature and show how demand rises on hot days. Measure error with MAE or MAPE and compare against a "same hour last week" baseline.

Source code: blentley/ForecastingElectricityDemand

32. Retail sales forecasting

Level: Intermediate | Skills: Prophet, holidays and promotions, multi-store forecasting | Dataset: Kaggle: Rossmann Store Sales, or M5 Forecasting (Walmart)

You forecast daily sales for retail stores using past sales, promotions, holidays, competition and store type. You learn Prophet for quick forecasts with seasonality and holiday effects, then compare it with a gradient boosting model that uses lag and calendar features. This is one of the most practical projects for retail, FMCG and e-commerce roles.

For a bigger challenge, the M5 Forecasting competition has hierarchical Walmart sales data at item, store and state level. For a festive-season twist that Indian recruiters will appreciate, add a holiday calendar with Diwali and other major festivals when you use Indian retail data.

Source code: LaurentVeyssier/time-serie-Sales-forecasting-with-Prophet

6. NLP projects

Short answer: NLP (natural language processing) projects teach you to turn messy text into numbers a model can use. Start with sentiment analysis and spam-style classification, then move to summarization, named entity recognition and chatbots. In 2026, most of these projects use a mix of classic tools (TF-IDF, scikit-learn, spaCy) and pretrained models from Hugging Face Transformers.

Text is everywhere in Indian companies: product reviews on Flipkart and Amazon, support tickets, WhatsApp chats with customers, resumes, news and social media. That is why NLP projects get good attention in interviews. A strong NLP project shows that you can clean text, choose a sensible baseline, and then explain when a transformer model is worth the extra cost.

A simple rule for every project in this section: build a quick baseline first (TF-IDF plus Logistic Regression is often enough), record its score, and only then try a transformer. Recruiters like to see that comparison.

33. Sentiment analysis of tweets and product reviews

Level: Beginner to Intermediate | Skills: Text cleaning, TF-IDF, Logistic Regression, Hugging Face pipelines | Dataset: Kaggle Sentiment140 (tweets) or any Amazon/Flipkart review dataset

You build a model that labels a piece of text as positive, negative or neutral. You learn the full journey: a rule-based method (VADER), a classic machine learning model, and a pretrained transformer. The project is small, but it teaches the core NLP workflow you will reuse everywhere.

Here is the transformer part. It runs on Google Colab in a few seconds:

from transformers import pipeline

clf = pipeline("sentiment-analysis",
               model="distilbert-base-uncased-finetuned-sst-2-english")

reviews = [
    "Delivery was fast and the phone works perfectly.",
    "Battery drains in 3 hours. Very disappointed.",
    "Packaging was okay, product is average.",
]
for text, out in zip(reviews, clf(reviews)):
    print(out["label"], round(out["score"], 3), "-", text)

This model only gives POSITIVE or NEGATIVE. Notice how it handles the third, neutral review. That is a good point to discuss in your README: why you might need a three-class model, and how you would fine-tune one (see project 64).

Source code: Naviden/Sentiment-Analysis-in-Python

34. Fake news detection

Level: Intermediate | Skills: TF-IDF, Passive Aggressive Classifier, LSTM, model evaluation | Dataset: Kaggle "Fake and Real News" dataset (ISOT)

You build a classifier that predicts whether a news article is real or fake from its title and body text. You learn how to handle long text, how to avoid data leakage (some datasets have source names inside the text, which makes the model "cheat"), and how to compare a linear model with an LSTM. Add a short section in your README on why such a model should support human fact-checkers, not replace them.

Source code: chethanhn29/NLP_Projects (see the FakeNews classifier folders)

35. Resume parser and job-matching tool

Level: Intermediate | Skills: PDF text extraction, spaCy, regex, cosine similarity | Dataset: Kaggle Resume Dataset

You build a tool that reads a resume (PDF or DOCX), pulls out the name, email, phone, skills, education and experience, and stores them as structured JSON. Then you compare each resume with a job description and give a match score. This is a very relatable project for Indian freshers, because most companies here use applicant tracking systems that do exactly this.

36. News article classification

Level: Beginner to Intermediate | Skills: Text vectorization, Naive Bayes, Linear SVM, confusion matrix | Dataset: AG News classification dataset (Kaggle)

You build a model that sorts news headlines and descriptions into categories such as World, Sports, Business and Sci/Tech. You learn multi-class classification, how to read a confusion matrix, and which categories the model mixes up (Business and Sci/Tech often overlap). For an Indian twist, scrape headlines from a few Indian news sites (respect their terms of use) and test whether your model still works on them.

37. Text summarization with transformers

Level: Intermediate | Skills: Hugging Face Transformers, BART/T5, ROUGE score | Dataset: CNN/DailyMail news dataset (on the Hugging Face Hub)

You build a tool that turns a long article into a short summary of three or four sentences. You learn the difference between extractive summarization (picking key sentences) and abstractive summarization (writing new sentences with a model like BART or T5). You also learn to measure quality with ROUGE and to check summaries by reading them yourself, because a high score does not always mean a faithful summary.

A nice extension is to summarize long Indian government reports or court judgments, which are public, and to explain how you handled documents longer than the model's input limit (split into chunks, summarize each, then summarize the summaries).

38. Named entity recognition (NER)

Level: Intermediate | Skills: spaCy, token classification, BERT, annotation | Dataset: CoNLL-2003, or your own labelled sentences

You build a model that finds names of people, organisations, places, dates and amounts in text. Start with spaCy's pretrained English model, then fine-tune it on your own domain, for example extracting company names and invoice amounts from bills, or drug names from medical notes. You learn how labelling works, how entity-level precision and recall are calculated, and why domain data matters more than a bigger model.

39. Chatbot for FAQs (rule-based to retrieval-based)

Level: Beginner | Skills: NLTK, TF-IDF, cosine similarity, intent classification | Dataset: Your own FAQ list (for example, a college admissions FAQ)

You build a chatbot that answers common questions. Version one is rule-based: it finds the stored FAQ answer most similar to the user's question using TF-IDF and cosine similarity. Version two adds intent classification so the bot knows whether the user is asking about fees, timings or placements. This is a good beginner project because it is easy to demo live in an interview. Once you are comfortable, upgrade it to the RAG chatbot in project 63.

Source code: parulnith/Building-a-Simple-Chatbot-in-Python-using-NLTK

40. YouTube comment toxicity detection

Level: Intermediate | Skills: Multi-label classification, class imbalance, BERT, YouTube Data API | Dataset: Kaggle Jigsaw Toxic Comment Classification Challenge

You build a model that flags toxic, insulting or threatening comments. The Jigsaw dataset is multi-label (one comment can be both "insult" and "obscene"), so you learn multi-label metrics and how to deal with rare classes. Then you use the YouTube Data API to pull comments from a public video and score them. Mention in your README how the model performs on Hinglish comments, because that is a real problem for Indian platforms.

7. Computer vision and deep learning projects

Short answer: Computer vision projects teach you to work with images and video using CNNs (convolutional neural networks), transfer learning and OpenCV. Begin with image classification on a standard dataset, then build something practical like face mask detection or pneumonia detection from X-rays. Use Google Colab or Kaggle Notebooks for free GPUs.

You can build every project here with either TensorFlow/Keras or PyTorch. Pick one and stay with it for your first few projects. Transfer learning (starting from a pretrained model such as MobileNetV2, ResNet50 or EfficientNet) is the default approach in 2026, because it gives good accuracy with small datasets and short training times.

41. Image classification with a CNN (CIFAR-10)

Level: Beginner to Intermediate | Skills: CNN layers, data augmentation, dropout, EarlyStopping | Dataset: CIFAR-10 (60,000 small colour images in 10 classes, built into Keras and torchvision)

You build a CNN that recognises objects such as aeroplanes, cats, ships and trucks. You learn how convolution and pooling layers work, why you normalise pixel values, and how data augmentation (flips, shifts, rotations) reduces overfitting. Then you replace your custom CNN with a pretrained model and compare accuracy and training time.

Source code: PatrickSVM/CIFAR10-Image-Classification-with-CNN

42. Real-time face mask detection

Level: Intermediate | Skills: OpenCV, transfer learning (MobileNetV2), face detection, webcam video | Dataset: Face Mask Detection dataset (Kaggle)

You build a system that finds faces in a live webcam feed and labels each one as "mask" or "no mask". You learn a two-stage pipeline: a face detector finds faces, then a classifier labels each face. The same pattern works for helmet detection on two-wheelers or safety-gear checks in factories, which are good Indian use cases to mention.

Source code: Piyush2912/Real-Time-Face-Mask-Detection

43. Facial emotion recognition

Level: Intermediate | Skills: CNNs, grayscale images, class imbalance, OpenCV | Dataset: FER-2013 (Kaggle)

You train a model that reads a face and predicts one of seven emotions, such as happy, sad, angry or surprised. FER-2013 images are small (48×48 grayscale) and some classes are rare, so you learn to handle imbalance with class weights or augmentation. Finish by running the model on your webcam. In the README, be honest about accuracy limits and the ethics of emotion detection.

Source code: atulapra/Emotion-detection

44. Pneumonia detection from chest X-rays

Level: Intermediate | Skills: Transfer learning, medical imaging, recall, Grad-CAM | Dataset: Chest X-Ray Images (Pneumonia), Kaggle

You build a classifier that labels chest X-rays as normal or pneumonia. The key lesson is choosing the right metric: in healthcare, missing a sick patient is worse than a false alarm, so you focus on recall and the confusion matrix, not only accuracy. Add Grad-CAM heatmaps to show which part of the X-ray the model looked at. That one image makes your project much more convincing in interviews.

45. Road lane line detection

Level: Beginner to Intermediate | Skills: OpenCV, Canny edge detection, Hough transform, video processing | Dataset: Dashcam videos (Udacity sample videos, or record your own)

You build a pipeline that draws lane lines on dashcam video, the first step toward self-driving car perception. You learn classic computer vision without deep learning: grayscale conversion, blurring, edge detection, a region of interest mask and the Hough line transform. Then test it on Indian roads, where lane markings are often faded, and explain where the simple method fails and why deep learning models are used in practice.

Source code: udacity/CarND-LaneLines-P1

46. Traffic sign recognition

Level: Intermediate | Skills: CNNs, multi-class classification (43 classes), augmentation | Dataset: GTSRB (German Traffic Sign Recognition Benchmark), available on Kaggle

You train a CNN to recognise traffic signs such as speed limits, stop and no entry. With 43 classes and uneven class sizes, you learn to balance data and to read a large confusion matrix. A good extension is to collect a few photos of Indian road signs and test how well the model transfers, then discuss what extra data you would need.

47. Handwritten digit and equation solver

Level: Intermediate | Skills: CNNs, image segmentation with OpenCV contours, MNIST, parsing | Dataset: MNIST plus a handwritten math symbols dataset (Kaggle)

Start with the classic: a CNN that reads handwritten digits from MNIST. Then extend it into an equation solver. You write "12 + 7 × 3" on paper, take a photo, split the image into single characters with OpenCV contours, classify each one, rebuild the expression and solve it. This project shows you can join several steps into a working product, which is more impressive than a single model.

48. Image generation with GANs

Level: Advanced | Skills: Generative Adversarial Networks, DCGAN, training stability | Dataset: MNIST, Fashion-MNIST or CelebA

You build a GAN in which a generator creates fake images and a discriminator tries to catch them. Over training, the generator learns to make realistic digits, clothes or faces. You learn why GANs are hard to train (mode collapse, unstable losses) and how DCGAN design choices help. In your README, briefly compare GANs with diffusion models, which power most image generation tools today.

Source code: mafda/generative_adversarial_networks_101

8. Recommendation system projects

Short answer: Recommendation projects show you can predict what a user will like next, the same problem Netflix, Amazon, Flipkart and Spotify solve every day. Build one content-based recommender (based on item features) and one collaborative filtering recommender (based on user behaviour), and explain the difference.

There are three main approaches you should know:

Evaluate your recommender with ranking metrics such as precision@k or recall@k, not only RMSE. And always handle the "cold start" problem: what do you show a brand-new user?

49. Movie recommendation system

Level: Beginner to Intermediate | Skills: TF-IDF, cosine similarity, matrix factorization (SVD) | Dataset: TMDB 5000 Movie Dataset (Kaggle) and MovieLens ratings

You build two recommenders. The first is content-based: it suggests movies with similar plot descriptions. The second uses MovieLens user ratings and collaborative filtering (SVD with the Surprise library, or implicit-feedback models) to suggest movies that similar users enjoyed. Here is the content-based core in a few lines:

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel

movies = pd.read_csv("tmdb_5000_movies.csv")
movies["overview"] = movies["overview"].fillna("")
matrix = TfidfVectorizer(stop_words="english").fit_transform(movies["overview"])
sim = linear_kernel(matrix, matrix)
index = pd.Series(movies.index, index=movies["title"])

def recommend(title, n=5):
    scores = sorted(enumerate(sim[index[title]]), key=lambda x: x[1], reverse=True)
    return movies["title"].iloc[[i for i, _ in scores[1:n + 1]]].tolist()

print(recommend("The Dark Knight"))

Improve it by adding genres, cast and director to the text, and wrap it in a Streamlit app that shows posters.

Source code: pratiknabriya/Recommender-System-Collaborative-Filtering-MovieLens

50. Book recommendation system

Level: Intermediate | Skills: User-item matrix, KNN, sparse data, popularity baseline | Dataset: Book-Crossing dataset (Kaggle)

You build a recommender that suggests books based on what readers with similar tastes rated highly. The Book-Crossing data is very sparse, so you learn to filter out users and books with very few ratings, build a sparse user-item matrix and use K-nearest neighbours to find similar books. Start with a simple "most popular books" baseline so you can show your model does better.

51. Product recommendation for e-commerce

Level: Intermediate to Advanced | Skills: Implicit feedback, session data, item-to-item similarity, ranking metrics | Dataset: Retailrocket e-commerce dataset (Kaggle)

You build "customers who viewed this also viewed" recommendations from real clickstream data: views, add-to-cart events and purchases. Unlike movie ratings, these are implicit signals, so you learn to weight events (a purchase counts more than a view) and to evaluate with precision@k on a time-based split. This is close to what e-commerce teams in Bengaluru and Hyderabad actually build.

52. Music recommendation engine

Level: Intermediate | Skills: Feature scaling, cosine similarity, clustering, playlist generation | Dataset: Spotify audio features (Kaggle)

You build a recommender that suggests songs with a similar "feel", using audio features such as danceability, energy, tempo and valence. You learn to scale features, find nearest songs with cosine similarity, and group songs into moods with K-Means. A fun extension: take a seed playlist and generate a 20-song playlist that keeps a similar mood, then show it on a Streamlit page.

9. Domain projects: healthcare, finance, e-commerce and HR

Short answer: Domain projects solve a real business problem in one industry, such as predicting hospital risk, insurance costs, customer value or cart abandonment. They help you stand out because they show business thinking, not just model building. Pick a domain that matches the jobs you want, and explain the business impact of your results in plain words.

Interviewers in Indian product companies, banks and analytics firms often ask, "So what would the business do with this model?" Domain projects let you answer that well. For each project below, write one paragraph in your README on who would use the output and what decision it supports. To see which skills each domain role expects, read our guide on data science skills.

53. Patient risk scoring system

Level: Intermediate | Skills: Classification, probability calibration, SHAP, healthcare metrics | Dataset: UCI "Diabetes 130-US Hospitals" dataset (hospital readmissions)

You build a model that gives each patient a risk score for being readmitted to hospital within 30 days. Instead of a yes/no label, you output a calibrated probability and group patients into low, medium and high risk. You learn to explain predictions with SHAP so a doctor can see why a patient is high risk (for example, many previous visits or a specific diagnosis).

54. Disease prediction from symptoms

Level: Beginner to Intermediate | Skills: Multi-class classification, one-hot features, Random Forest | Dataset: Diseases and Symptoms dataset (Kaggle)

You build a model that takes a list of symptoms (fever, cough, headache and so on) and suggests the most likely diseases. You learn to work with many binary features and many output classes, and to show the top three predictions with probabilities rather than a single answer. Add a clear disclaimer in the app that it is a learning project and not medical advice.

55. Health insurance claim cost prediction

Level: Beginner to Intermediate | Skills: Regression, feature interactions, residual analysis | Dataset: Medical cost dataset (insurance.csv) from stedy/Machine-Learning-with-R-datasets

You predict yearly medical insurance charges from age, BMI, number of children, smoking status and region. The EDA alone is interesting: smoking has a huge effect, and smoking combined with high BMI is even bigger. You learn why feature interactions matter, how to compare Linear Regression with tree models, and how to read residual plots. Relate it to how Indian health insurers price policies.

56. Stock portfolio optimization

Level: Intermediate to Advanced | Skills: Returns and risk, covariance, Modern Portfolio Theory, Sharpe ratio | Dataset: NSE stock prices via yfinance or the NSE historical data on Kaggle

You take 10 to 15 NSE stocks, calculate daily returns, and find the mix of weights that gives the best return for a given level of risk. You learn the efficient frontier, the Sharpe ratio and how to use the PyPortfolioOpt library or plain NumPy and SciPy. Backtest your portfolio on a later time period and compare it with the Nifty 50. Make it clear that this is an academic exercise about methods, not investment advice.

57. Customer lifetime value (CLV) prediction

Level: Intermediate | Skills: RFM analysis, BG/NBD and Gamma-Gamma models, regression | Dataset: UCI "Online Retail II" transactions dataset

You estimate how much revenue each customer will bring over the next 6 or 12 months. You start with RFM (recency, frequency, monetary) features, then use probabilistic models (the lifetimes library or PyMC-Marketing) or a regression model to predict future spend. You learn how marketing teams use CLV to decide how much to spend on keeping a customer, and you can segment customers into "protect", "grow" and "low priority" groups.

58. Shopping cart abandonment predictor

Level: Intermediate | Skills: Session features, imbalanced classification, threshold tuning | Dataset: UCI "Online Shoppers Purchasing Intention" dataset

You predict whether an online session will end in a purchase or be abandoned, using pages visited, time spent, bounce rates, month and visitor type. Only a small share of sessions convert, so you learn to deal with class imbalance and to choose a threshold based on business cost. End with a clear recommendation, for example, "send a discount reminder to sessions with an abandonment score above 0.7".

59. Aspect-based product review analysis

Level: Intermediate | Skills: Aspect extraction, zero-shot classification, topic modelling, dashboards | Dataset: Amazon or Flipkart product reviews (several datasets are on Kaggle)

Project 33 tells you whether a review is positive or negative. This project goes deeper: it tells you what customers like or dislike, such as battery, camera, delivery or price. You extract aspects with spaCy noun phrases or a zero-shot transformer model, score sentiment per aspect, and show results in a Power BI or Streamlit dashboard. Product managers love this kind of output, so frame your results as product insights.

60. HR analytics: workforce and attrition dashboard

Level: Beginner to Intermediate | Skills: EDA, KPI design, Power BI or Streamlit, basic statistics | Dataset: IBM HR Analytics dataset (Kaggle) or the Human Resources dataset (Kaggle)

Attrition prediction is covered in the machine learning section, so here you focus on the analytics side. You build an HR dashboard showing headcount, attrition rate by department, salary bands, performance scores, overtime and diversity. You learn to define KPIs clearly, run simple tests (for example, is attrition really higher among employees who work overtime?), and present findings the way an HR head would want to read them.

Source code: Geo-y20/IBM-HR-Analysis

61. Adaptive learning path recommender (education)

Level: Advanced | Skills: Knowledge tracing, sequence data, recommendation | Dataset: EdNet (riiid/ednet), a large public log of student interactions with an AI tutor

You predict whether a student will answer the next question correctly, based on their past answers. This is called knowledge tracing. Using these predictions, you recommend the next question or topic, so weak areas get more practice. You learn to work with large sequence data and to evaluate with AUC. EdTech is a big employer in India, so this project fits well if you want to work in that space.

62. MOOC course feedback analysis and course recommender

Level: Beginner to Intermediate | Skills: Sentiment analysis, topic modelling (BERTopic or LDA), content-based recommendation | Dataset: edX courses dataset (Kaggle) plus public course reviews

You build two things. First, a feedback analyser that finds the main themes in course reviews (too fast, great examples, poor audio) and their sentiment. Second, a content-based recommender that suggests courses with similar descriptions and levels. You learn topic modelling and how to combine NLP with recommendations in one small product.

10. Advanced projects (GenAI, big data and deployment)

Short answer: Advanced projects show you can work like an industry data scientist: building GenAI apps with large language models, processing big data with PySpark, and deploying and monitoring models. One well-documented advanced project, live on the internet, often does more for your resume than five beginner notebooks.

These projects take more time, often two to four weeks each. Choose one or two that match your target role. If you want to go deeper into the tools used here, our data science tool list explains each one.

63. RAG chatbot that answers questions from your PDFs

Level: Advanced | Skills: LLMs, embeddings, vector databases, LangChain or LlamaIndex, prompt design | Dataset: Any set of PDFs (college syllabus, company policies, RBI circulars)

You build a chatbot that answers questions using only the content of your documents. This pattern is called retrieval-augmented generation (RAG), and it is one of the most common GenAI use cases in Indian companies today. You learn how to split documents into chunks, turn them into embeddings, store them in a vector database, retrieve the most relevant chunks and pass them to an LLM with a clear prompt.

The test set in step 5 is what makes this project stand out. Most RAG demos skip evaluation, so show answer accuracy and how often the bot correctly says "I don't know".

64. Fine-tune a small transformer for text classification

Level: Advanced | Skills: Hugging Face Transformers, Datasets and Trainer, tokenization, PEFT/LoRA | Dataset: AG News, or your own labelled support tickets

You fine-tune a small model such as DistilBERT on a labelled dataset, for example to route customer support tickets to the right team. You learn tokenization, training on a GPU in Colab, choosing learning rate and epochs, and comparing the result with your TF-IDF baseline. Then try parameter-efficient fine-tuning (LoRA with the PEFT library) and compare memory use and accuracy. Push the final model to the Hugging Face Hub with a model card.

65. Big data pipeline with PySpark

Level: Advanced | Skills: PySpark DataFrames, Spark SQL, partitioning, Parquet | Dataset: NYC Yellow Taxi trip data (Kaggle)

You process millions of taxi trips with PySpark. You clean bad records (negative fares, zero distance), build features such as pickup hour and trip speed, and save the clean data as partitioned Parquet files. Then you answer business questions with Spark SQL and train a fare prediction model with Spark MLlib. Run it on Databricks Free Edition or a local Spark install. This project is a bridge between data science and data engineering roles.

66. Real-time streaming analytics

Level: Advanced | Skills: Apache Kafka, Spark Structured Streaming, windowed aggregations, dashboards | Dataset: A simulated stream (for example, fake UPI transactions or sensor readings generated by a Python script)

You build a pipeline where events flow in continuously and results update every few seconds. A Python producer sends events to Kafka, Spark Structured Streaming reads them, calculates rolling counts and averages over time windows, and flags unusual events (for example, many transactions from one account in a minute). You learn the difference between batch and streaming, and how checkpoints keep a stream reliable.

Source code: RichardAfolabi/Realtime-Data-Analytics-Using-Spark

67. End-to-end ML deployment with FastAPI, Streamlit and Docker

Level: Intermediate to Advanced | Skills: Model serialization, REST APIs, FastAPI, Docker, cloud hosting | Dataset: Loan Prediction dataset (Kaggle), or any model you built earlier

You take a model you already trained (loan approval works well) and turn it into a service other people can use. You save the model with joblib, serve it with FastAPI, build a Streamlit front end that calls the API, and package everything with Docker so it runs the same on any machine. Here is a minimal API:

from fastapi import FastAPI
from pydantic import BaseModel
import joblib

app = FastAPI()
model = joblib.load("loan_model.joblib")

class Applicant(BaseModel):
    income: float
    loan_amount: float
    credit_history: int

@app.post("/predict")
def predict(a: Applicant):
    features = [[a.income, a.loan_amount, a.credit_history]]
    return {"approved": int(model.predict(features)[0])}

Run it with uvicorn main:app --reload and open the automatic docs page at /docs to test it. Then write a Dockerfile, deploy the container to a free or low-cost host (Render, Hugging Face Spaces or a cloud free tier), and put the live link at the top of your README.

68. MLOps pipeline with MLflow

Level: Advanced | Skills: Experiment tracking, model registry, reproducibility, CI with GitHub Actions | Dataset: Any tabular project, such as churn or credit risk

You take one of your earlier projects and add proper MLOps. Every training run logs its parameters, metrics and model to MLflow, so you can compare runs and promote the best model in the MLflow Model Registry. Then add a GitHub Actions workflow that runs tests and retrains when data changes, and a simple drift check (for example with Evidently) that alerts you when new data looks different from training data. You learn how models are managed after the notebook, which many freshers never show.

69. A/B testing analysis

Level: Intermediate | Skills: Hypothesis testing, p-values, confidence intervals, sample size | Dataset: Any A/B test results dataset (several are on Kaggle), or simulated data

You analyse an experiment where half the users saw an old web page and half saw a new one. You check whether the new page really increased conversions or whether the difference is just chance. You learn hypothesis testing, sample size planning and common mistakes, such as stopping the test early. Here is the core test:

from statsmodels.stats.proportion import proportions_ztest, proportion_confint

conversions = [480, 540] # old page, new page
visitors = [10000, 10000]

stat, p_value = proportions_ztest(conversions, visitors)
print(f"z = {stat:.2f}, p-value = {p_value:.4f}")

for name, c, n in zip(["old", "new"], conversions, visitors):
    low, high = proportion_confint(c, n)
    print(f"{name}: {c / n:.2%} (95% CI {low:.2%} to {high:.2%})")

End with a business decision in one sentence: launch, do not launch, or run longer. Product analyst roles in Indian startups ask about A/B testing very often.

Source code: ahujaya/Analyze-AB-Test-Results-Python

70. Reinforcement learning agent that plays a game

Level: Advanced | Skills: Q-learning, Deep Q-Networks (DQN), Gymnasium environments, reward design | Dataset: No dataset needed; the agent learns by playing (Gymnasium CartPole, a grid world or Snake)

You build an agent that learns to play a game through trial and error. Start with tabular Q-learning on a small grid world, then move to a DQN on CartPole using Gymnasium (the maintained successor to OpenAI Gym). You learn rewards, exploration versus exploitation and why training curves are noisy. Record a short video of your agent before and after training; it makes a great LinkedIn post.

Source code: amjadmajid/deep-reinforcement-learning-games-from-scratch

11. Final-year data science project ideas

Short answer: A good final-year project solves a real problem, uses real data, and ends with a working demo your examiners can try. Choose something your guide can understand, that fits in one semester, and that has at least one original part (your own data, a local Indian angle, or a deployed app).

Here are 15 final-year project ideas that work well for B.Tech, BCA, MCA and M.Sc students:

Useful public data for some of these: district-wise crop production in India, India road accident data, brain tumour MRI images, email campaign data for SMEs, top Instagram influencers and data.gov.in for government data.

Three tips for choosing and presenting your final-year project:

12. How to build a data science project step by step

Short answer: Every data science project follows the same eight steps: define the question, get the data, clean it, explore it, build features, train a model, evaluate it, and then deploy and document. Following this order keeps you focused and makes your project easy for a recruiter to understand.

Use this workflow for any project in this post, from Titanic to a RAG chatbot. The steps match what is covered in a typical data science course syllabus, so each project also becomes revision.

README checklist

A good README is the first thing a recruiter reads. Include these sections:

13. How to showcase projects on GitHub, LinkedIn and your resume

Short answer: Put your best three to five projects on GitHub with clean READMEs, pin them on your profile, share short LinkedIn posts about what you learned, and write resume bullets that show the problem, your method and a measurable result. Quality and clear explanation matter far more than the number of projects.

On GitHub

On LinkedIn

On your resume

List two to four projects under a "Projects" heading, with the strongest one first. Each project gets one line with the title and tools, then two or three bullets. Every bullet should follow the pattern: action verb, what you built, how, and the result. If you do not have a business number, use a model metric or a scale number (rows processed, users served).

Sample bullets you can adapt (use your own real numbers):

The numbers in these samples are examples only. Always use the real figures from your own project, and be ready to explain every one of them in an interview. If you are planning your next step after building projects, our page on data science jobs in Hyderabad shows the roles and skills companies are hiring for.

14. How Brolly Academy helps

Short answer: Our data science course takes you from Python and statistics to machine learning, deep learning and real-time projects, with placement assistance.

Frequently asked questions