Free Machine Learning Projects With Datasets You Can Start Today

The most honest thing anyone can tell you about learning machine learning is that tutorials will only take you so far. You can watch videos, follow along with notebooks, and reproduce exactly what an instructor types. But the moment someone asks you to build something from scratch on a dataset they hand you, with a business problem they describe and no step-by-step guide, the gap between knowing machine learning and being able to do machine learning becomes very clear very fast.

Projects close that gap. Not tutorials, not certificates, not courses. Projects where you take a dataset, define the problem, write the code, evaluate the output, and document what you found. This guide gives you the best free machine learning projects organized by difficulty level, each with a free dataset you can download today and an explanation of what the project teaches and why hiring managers actually care about it.

Before You Pick a Project, Understand What You Are Actually Building

Every machine learning project belongs to one of a small number of problem types. Knowing which type your project is determines which algorithms you try, how you evaluate success, and what the output looks like.

Regression problems predict a continuous number. House price prediction, salary estimation, and energy consumption forecasting are all regression problems. The output is a number and evaluation metrics include mean absolute error and root mean squared error.

Classification problems predict a category. Customer churn prediction, spam detection, fraud detection, and disease diagnosis are all classification problems. The output is a class label and evaluation metrics include accuracy, precision, recall, and F1 score. Class imbalance, where one category is far rarer than the other, is one of the most common challenges in real-world classification projects and learning to handle it correctly is what separates beginner projects from professional ones.

Clustering problems group data points by similarity without predefined labels. Customer segmentation, anomaly detection, and document grouping are clustering problems. There is no single right answer to evaluate against, which makes these both more flexible and harder to validate.

Natural language processing projects work with text data. Sentiment analysis, topic modeling, and text classification all fall here. These projects require different preprocessing than numeric data, involving tokenization, stopword removal, and vectorization, which makes them a meaningful step up in complexity.

Know which type you are building before you start. It determines everything else.

Project 1: Customer Churn Prediction (Beginner)

This is one of the most commonly requested machine learning use cases in real business and one of the best starting projects for a portfolio because the business problem is immediately understandable to anyone in an interview.

The problem: A telecom company wants to predict which customers are likely to cancel their subscription before they do, so the retention team can reach out proactively with an offer. Reducing churn by even one percent can represent millions in annual recurring revenue, which is why this project type appears across every subscription-based industry from software to insurance to media.

Dataset: The Telecom Customer Churn dataset on Kaggle contains 7,043 customer records with demographic information, account details like contract type and monthly charges, and a binary churn label. Download link: kaggle.com/datasets/blastchar/telco-customer-churn

What you build: A binary classification model that predicts whether a customer will churn. Start with logistic regression to establish a baseline, then try a random forest classifier and compare performance. The most important technical challenge in this project is handling class imbalance since churned customers are a minority of the dataset. Use SMOTE from the imbalanced-learn library to oversample the minority class and compare your precision and recall before and after.

Starter code:

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix
from imblearn.over_sampling import SMOTE

# Load data
df = pd.read_csv('telco_churn.csv')

# Drop customer ID column
df.drop('customerID', axis=1, inplace=True)

# Convert TotalCharges to numeric
df['TotalCharges'] = pd.to_numeric(df['TotalCharges'], errors='coerce')
df.dropna(inplace=True)

# Encode categorical columns
le = LabelEncoder()
for col in df.select_dtypes(include='object').columns:
    df[col] = le.fit_transform(df[col])

# Split features and target
X = df.drop('Churn', axis=1)
y = df['Churn']

# Handle class imbalance with SMOTE
sm = SMOTE(random_state=42)
X_resampled, y_resampled = sm.fit_resample(X, y)

# Train test split
X_train, X_test, y_train, y_test = train_test_split(
    X_resampled, y_resampled, test_size=0.2, random_state=42
)

# Baseline logistic regression
lr = LogisticRegression(max_iter=1000)
lr.fit(X_train, y_train)
print("Logistic Regression Results:")
print(classification_report(y_test, lr.predict(X_test)))

# Random forest
rf = RandomForestClassifier(n_estimators=100, random_state=42)
rf.fit(X_train, y_train)
print("Random Forest Results:")
print(classification_report(y_test, rf.predict(X_test)))

What it signals to hiring managers: You understand class imbalance and know how to address it. You can compare multiple models against a baseline and interpret precision and recall in a business context rather than just reporting accuracy. Customer retention is one of the highest-value ML applications in any subscription business.

Project 2: House Price Prediction (Beginner)

Regression is one of the first ML concepts every analyst learns and a house price prediction project is the most accessible way to demonstrate it because the target variable is immediately intuitive and the features map to real-world understanding.

Dataset: The Ames Housing dataset from Kaggle is the most feature-rich house price dataset available for learning purposes. It contains 79 explanatory variables describing almost every aspect of residential homes in Ames, Iowa, with 1,460 training records. Download link: kaggle.com/competitions/house-prices-advanced-regression-techniques

What you build: A regression model that predicts house sale prices. The real learning in this project is not the model itself but the feature engineering and preprocessing work that precedes it. The dataset has missing values in multiple columns, some categorical and some numeric, and deciding how to handle each one requires understanding why the values are missing. A garage area of zero might mean no garage, not missing data. A pool quality recorded as NaN almost certainly means no pool.

Starter code:

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error

df = pd.read_csv('train.csv')

# Fill numeric missing values with median
numeric_cols = df.select_dtypes(include=[np.number]).columns
df[numeric_cols] = df[numeric_cols].fillna(df[numeric_cols].median())

# Fill categorical missing values with 'None' (most represent absence)
categorical_cols = df.select_dtypes(include='object').columns
df[categorical_cols] = df[categorical_cols].fillna('None')

# Encode categorical features
le = LabelEncoder()
for col in categorical_cols:
    df[col] = le.fit_transform(df[col].astype(str))

# Log transform target to reduce skewness
df['SalePrice'] = np.log1p(df['SalePrice'])

X = df.drop(['Id', 'SalePrice'], axis=1)
y = df['SalePrice']

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

model = GradientBoostingRegressor(n_estimators=300, learning_rate=0.05, random_state=42)
model.fit(X_train, y_train)

preds = model.predict(X_test)
mae = mean_absolute_error(y_test, preds)
rmse = np.sqrt(mean_squared_error(y_test, preds))

print(f"MAE: {mae:.4f}")
print(f"RMSE: {rmse:.4f}")

What it signals: Feature engineering skills, understanding of missing value strategies, and the ability to handle a high-dimensional real-world dataset. Log-transforming the target variable to handle skewed distributions is the kind of detail that tells interviewers you have done this before and not just followed a tutorial.

Project 3: Credit Card Fraud Detection (Intermediate)

Fraud detection is one of the most commercially important ML applications in the world and one of the most technically challenging beginner-to-intermediate projects because the class imbalance is extreme. In most fraud datasets, fraudulent transactions represent less than 0.2 percent of all records. A model that predicts everything as non-fraud achieves 99.8 percent accuracy and is completely useless. Learning to measure and optimize for precision and recall instead of accuracy is the central lesson of this project.

Dataset: The Credit Card Fraud Detection dataset on Kaggle contains 284,807 transactions made by European cardholders, with 492 of them labeled as fraudulent. Features are anonymized using PCA except for the transaction amount and time columns. Download link: kaggle.com/datasets/mlg-ulb/creditcardfraud

What you build: A binary classifier that maximizes recall on the fraud class, meaning it catches as many real frauds as possible, while keeping precision high enough that legitimate transactions are not flagged at an unacceptable rate. Use precision-recall AUC as your primary evaluation metric rather than accuracy.

Starter code:

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, precision_recall_curve, auc
import matplotlib.pyplot as plt

df = pd.read_csv('creditcard.csv')

# Scale Amount and Time columns
scaler = StandardScaler()
df['Amount'] = scaler.fit_transform(df[['Amount']])
df['Time'] = scaler.fit_transform(df[['Time']])

X = df.drop('Class', axis=1)
y = df['Class']

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

# Use class_weight to handle imbalance
model = RandomForestClassifier(
    n_estimators=100,
    class_weight='balanced',
    random_state=42
)
model.fit(X_train, y_train)

print(classification_report(y_test, model.predict(X_test)))

# Precision-recall curve
probs = model.predict_proba(X_test)[:, 1]
precision, recall, _ = precision_recall_curve(y_test, probs)
pr_auc = auc(recall, precision)
print(f"Precision-Recall AUC: {pr_auc:.4f}")

What it signals: You understand the limitations of accuracy on imbalanced datasets. You know how to use stratified splits to maintain class ratios in train and test sets. You can interpret precision-recall tradeoffs in a business context where catching every fraud matters but so does not blocking legitimate customers.

Project 4: Sentiment Analysis on Product Reviews (Intermediate)

Natural language processing appears in a growing number of data analyst and data science job descriptions and sentiment analysis is the most accessible NLP project to build because the output is immediately interpretable and the business application is obvious. Companies spend significant resources trying to understand what customers think about their products at scale. A sentiment classifier automates the first layer of that understanding.

Dataset: The Amazon Product Reviews dataset on Kaggle covers millions of reviews across multiple product categories. For a manageable starting size, use the subset filtered to electronics or books with star ratings that you will convert into sentiment labels. Download link: kaggle.com/datasets/arhamrumi/amazon-product-reviews

What you build: A text classification model that labels reviews as positive, neutral, or negative based on the review text. Map star ratings of 4 and 5 to positive, 3 to neutral, and 1 and 2 to negative. Preprocess text by converting to lowercase, removing punctuation and stopwords, and vectorizing using TF-IDF. Try a Naive Bayes classifier as the baseline and a logistic regression with TF-IDF as the main model.

Starter code:

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
import re

df = pd.read_csv('amazon_reviews.csv')
df = df[['reviewText', 'overall']].dropna()

# Map star ratings to sentiment labels
def map_sentiment(rating):
    if rating >= 4:
        return 'positive'
    elif rating == 3:
        return 'neutral'
    else:
        return 'negative'

df['sentiment'] = df['overall'].apply(map_sentiment)

# Basic text cleaning
def clean_text(text):
    text = text.lower()
    text = re.sub(r'[^a-z\s]', '', text)
    return text

df['clean_review'] = df['reviewText'].apply(clean_text)

X = df['clean_review']
y = df['sentiment']

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

tfidf = TfidfVectorizer(max_features=10000, ngram_range=(1, 2))
X_train_tfidf = tfidf.fit_transform(X_train)
X_test_tfidf = tfidf.transform(X_test)

model = LogisticRegression(max_iter=1000, class_weight='balanced')
model.fit(X_train_tfidf, y_train)

print(classification_report(y_test, model.predict(X_test_tfidf)))

What it signals: You can work with unstructured text data, which is a distinct and valued skill from tabular data work. You understand TF-IDF vectorization and can explain what it does and why it produces better results than simple word counts. Sentiment analysis projects connect directly to customer analytics, product analytics, and brand monitoring roles.

Project 5: Sales Forecasting With Time Series (Intermediate to Advanced)

Forecasting is one of the most in-demand ML applications across retail, finance, logistics, and supply chain industries. A time series project demonstrates that you can work with data where the temporal order of observations matters and standard train-test splitting on random rows would produce misleading results.

Dataset: The Walmart Sales Forecasting dataset on Kaggle contains weekly sales data for 98 products across 45 stores with additional data on holiday events, temperature, fuel prices, and unemployment rate. Download link: kaggle.com/competitions/walmart-recruiting-store-sales-forecasting

What you build: A sales forecasting model that predicts weekly sales for each department. Start with feature engineering to extract time-based features from the date column including week of year, month, quarter, and whether the week contains a holiday. Train a gradient boosting model using LightGBM and evaluate using mean absolute error on a holdout of the last three months of data. The key rule in time series validation is that your test set must always be in the future relative to your training set.

Starter code:

import pandas as pd
import numpy as np
import lightgbm as lgb
from sklearn.metrics import mean_absolute_error

train = pd.read_csv('train.csv')
features = pd.read_csv('features.csv')
stores = pd.read_csv('stores.csv')

# Merge all data
df = train.merge(features, on=['Store', 'Date', 'IsHoliday'])
df = df.merge(stores, on='Store')
df['Date'] = pd.to_datetime(df['Date'])

# Extract time features
df['week'] = df['Date'].dt.isocalendar().week.astype(int)
df['month'] = df['Date'].dt.month
df['year'] = df['Date'].dt.year
df['quarter'] = df['Date'].dt.quarter

# Time-based split: train on earlier dates, test on later dates
split_date = '2012-08-01'
train_df = df[df['Date'] < split_date]
test_df = df[df['Date'] >= split_date]

feature_cols = ['Store', 'Dept', 'week', 'month', 'year', 'quarter',
                'IsHoliday', 'Temperature', 'Fuel_Price', 'Size']

X_train = train_df[feature_cols].fillna(0)
y_train = train_df['Weekly_Sales']
X_test = test_df[feature_cols].fillna(0)
y_test = test_df['Weekly_Sales']

model = lgb.LGBMRegressor(n_estimators=500, learning_rate=0.05, random_state=42)
model.fit(X_train, y_train)

preds = model.predict(X_test)
print(f"MAE: {mean_absolute_error(y_test, preds):,.2f}")

What it signals: You understand time series validation and the critical rule that test data must always follow training data chronologically. You can handle multi-source data merging and feature engineering on dates. LightGBM is a production-grade library used by companies like Booking.com and Uber for their forecasting pipelines.

Project 6: Customer Segmentation With K-Means Clustering (Intermediate)

Most ML projects in portfolios are supervised, meaning they have labeled targets. Clustering is unsupervised and it demonstrates a different kind of analytical thinking, finding structure in data without being told what to look for. Customer segmentation is the most commercially relevant clustering application and the most explainable to a non-technical hiring manager.

Dataset: The Online Retail dataset from the UCI Machine Learning Repository contains 541,909 transactions from a UK-based online retailer. Use it to build RFM features: recency of last purchase, frequency of total purchases, and monetary value of total spend. Download link: archive.ics.uci.edu/dataset/352/online+retail

What you build: A K-means clustering model that segments customers into groups based on their RFM scores. Use the elbow method and silhouette score to choose the optimal number of clusters. Interpret each cluster in business terms: high-value loyal customers, at-risk customers who have not purchased recently, and new low-spend customers with growth potential.

Starter code:

import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
import matplotlib.pyplot as plt

df = pd.read_excel('Online Retail.xlsx')
df.dropna(subset=['CustomerID'], inplace=True)
df = df[df['Quantity'] > 0]
df['TotalSpend'] = df['Quantity'] * df['UnitPrice']
df['InvoiceDate'] = pd.to_datetime(df['InvoiceDate'])

snapshot_date = df['InvoiceDate'].max() + pd.Timedelta(days=1)

rfm = df.groupby('CustomerID').agg(
    Recency=('InvoiceDate', lambda x: (snapshot_date - x.max()).days),
    Frequency=('InvoiceNo', 'nunique'),
    Monetary=('TotalSpend', 'sum')
).reset_index()

scaler = StandardScaler()
rfm_scaled = scaler.fit_transform(rfm[['Recency', 'Frequency', 'Monetary']])

# Elbow method
inertias = []
for k in range(2, 11):
    km = KMeans(n_clusters=k, random_state=42, n_init=10)
    km.fit(rfm_scaled)
    inertias.append(km.inertia_)

# Fit final model with chosen k
k_optimal = 4
km = KMeans(n_clusters=k_optimal, random_state=42, n_init=10)
rfm['Cluster'] = km.fit_predict(rfm_scaled)

print(rfm.groupby('Cluster')[['Recency', 'Frequency', 'Monetary']].mean())

What it signals: You can work with unsupervised methods and more importantly you can interpret and name clusters in business language. The business interpretation of clusters is more valuable than the technical model, and knowing how to communicate what each segment means to a marketing team shows analytical maturity.

How to Present ML Projects in a Portfolio

Each project needs four things to work as a portfolio piece. A clear problem statement written in business language, not ML jargon, at the top of the notebook. A documented data exploration section that shows you looked at the data before building anything. An evaluation section that interprets the metrics in the context of the business problem rather than just printing numbers. And a summary section that states your key findings and what a business would do differently based on the model output.

Jupyter notebooks are the standard format for ML portfolio projects. Host them on GitHub and write a README that links directly to each notebook with a one-sentence description of the business problem and the model used. Do not upload notebooks with unrun cells or broken outputs. Run everything from top to bottom, confirm the outputs are clean, and then commit.

Machine Learning Projects Cheat Sheet

Project	Type	Dataset	Key Library	Skill Demonstrated
Customer Churn	Classification	Telco Churn, Kaggle	scikit-learn, imbalanced-learn	Class imbalance, F1 optimization
House Price Prediction	Regression	Ames Housing, Kaggle	scikit-learn	Feature engineering, missing values
Fraud Detection	Classification	Credit Card Fraud, Kaggle	scikit-learn	Extreme imbalance, precision-recall
Sentiment Analysis	NLP Classification	Amazon Reviews, Kaggle	scikit-learn, NLTK	Text preprocessing, TF-IDF
Sales Forecasting	Time Series Regression	Walmart Sales, Kaggle	LightGBM	Time-based splits, date features
Customer Segmentation	Clustering	Online Retail, UCI	scikit-learn	RFM, K-means, cluster interpretation

Common Mistakes to Avoid

Evaluating classification models with accuracy alone. On imbalanced datasets accuracy is a misleading metric. A model that predicts everything as the majority class can have 98 percent accuracy while detecting zero fraud cases. Always report precision, recall, and F1 score for classification problems and explain what each means in the context of the business problem.

Using random train-test splits for time series data. Splitting randomly on a time-ordered dataset means some of your training data will be from dates after your test data. The model learns from the future to predict the past, which produces results that look great in evaluation and fail completely in production. Always split chronologically.

Copying a tutorial notebook and submitting it as a project. Interviewers and hiring managers recognize popular tutorials. The Titanic notebook and the standard house price tutorial have been submitted thousands of times. Follow the tutorial to understand the approach, then apply it to a different dataset or add original feature engineering and a question the tutorial did not ask. That addition is what makes the project yours.

Skipping the interpretation step. A notebook that ends with a classification report and nothing else tells the interviewer what the model achieved but not what it means. Add two or three sentences that translate the numbers into business language. Eighty-three percent recall on the fraud class means the model catches 83 percent of all fraudulent transactions, which at the current transaction volume would flag approximately 400 additional frauds per month that the current rule-based system misses.

Building model accuracy without building the model explanation. In 2026, knowing why a model makes a prediction is as important as the prediction itself in most business contexts. Add SHAP value analysis or feature importance plots to at least one project in your portfolio. One chart showing which features drive churn prediction most strongly communicates business insight that goes beyond the model accuracy number.

Machine learning projects build the skills that courses describe. The difference between a candidate who has watched 40 hours of ML videos and one who has completed four end-to-end projects on real data is visible in the first ten minutes of a technical interview. Start with the beginner projects, build the habits of proper evaluation and documentation, and move to the intermediate projects as the patterns start to feel familiar. The portfolio will follow.

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