■ TechHorizon Internship – Machine Learning Projects
■ Objective:
As a Machine Learning Intern at TechHorizon, you will work on practical and impactful ML problems using
real-world datasets. This internship is designed to strengthen your foundations, improve your problem-solving
skills, and give you hands-on project experience.
■ General Instructions:
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Complete all 3 tasks within the internship period.
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Submit your work in a well-documented Jupyter Notebook or Python script.
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Include a short report (1–2 pages) summarizing: - Your approach for each task - Challenges faced - Key
learnings and model performance
■ TASKS
Task 1: Fake News Detection
Goal: Build a machine learning model to classify news articles as fake or real.
Dataset: Fake and Real News Dataset (https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset)
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Preprocess text (removal of stopwords, lemmatization/stemming).
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Use classifiers like Logistic Regression, Naïve Bayes, or Random Forest.
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Evaluate your model using accuracy, precision, and F1-score.
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(Optional): Create a basic Streamlit app for testing news snippets.
Task 2: Customer Segmentation Using Clustering
Goal: Segment customers based on demographic and spending data using unsupervised learning.
Dataset: Mall Customer Segmentation Data
(https://www.kaggle.com/vjchoudhary7/customer-segmentation-tutorial-in-python)
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Perform EDA and feature scaling.
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Apply K-Means or Hierarchical clustering.
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Visualize clusters and explain business insights.
Task 3: Sentiment Analysis of Movie Reviews
Goal: Create a model that classifies movie reviews as positive or negative.
Dataset: IMDb Movie Reviews Dataset
(https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews)
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Text preprocessing: tokenization, removing stopwords, etc.
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Train an ML model (SVM, Naïve Bayes, or Logistic Regression).
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Evaluate with accuracy and F1-score.
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(Optional): Add a basic interface to test custom reviews.
■ Submission Guidelines
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Code: Submit clean, well-commented Python scripts or Jupyter Notebooks.
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Report: 1–2 pages covering approach, challenges, and outcomes.
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Deployment (Optional): Share a link or instructions if you deployed any app.
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