Step-by-Step: Build a Simple Recommendation System

Step-by-Step: Build a Simple Recommendation System. Get practical lessons and hands-on examples at AIComputerClasses in Indore to master programming & IT development skills quickly. Includes references to tools like ChatGPT, Power BI, Excel, Figma, or Python where appropriate. Ideal for beginners and working professionals seeking fast skill gains. Follow practical exercises and tool-based examples to learn rapidly.

 Step-by-Step: Build a Simple Recommendation System

Recommendation systems are the hidden engines behind Netflix’s movie suggestions, Amazon’s “People also bought” section, and Spotify’s personalized playlists.

At AI Computer Classes – Indore, you’ll learn how to build your own recommendation system — from basic logic to implementing it with Python and real-world datasets. This guide walks you through every step, making it ideal for beginners and professionals eager to explore AI-driven development.

🚀 What Is a Recommendation System?

A recommendation system filters and predicts what users might like based on patterns, preferences, and data.

There are mainly two types:

1. Content-Based Filtering – Recommends items similar to those a user liked before.
2. Collaborative Filtering – Recommends items liked by users with similar interests.

🧩 Example:

• Netflix suggests movies similar to what you’ve watched (content-based).
• Amazon shows products others with similar purchases also bought (collaborative).

🧩 Step 1: Prepare Your Dataset

You can start with a simple dataset — for example, movie ratings.

UserMovieRatingAInception5ATitanic4BInception4BAvatar5CTitanic3

💡 Use Excel or Power BI to quickly view and clean the dataset before feeding it into Python.

🧠 Step 2: Import Required Libraries

Use Python libraries to manage data and build your model.

import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity

Load your dataset:

data = pd.read_csv("movies.csv")

🧮 Step 3: Create a User-Item Matrix

Transform the data into a pivot table:

user_movie_matrix = data.pivot_table(index='User', columns='Movie', values='Rating').fillna(0)

This matrix shows which user rated which movie — a foundation for similarity-based recommendations.

📊 Step 4: Compute Similarity Between Users

Use cosine similarity to identify users with similar tastes:

similarity = cosine_similarity(user_movie_matrix)
similarity_df = pd.DataFrame(similarity, index=user_movie_matrix.index, columns=user_movie_matrix.index)

💬 Example: If users A and B have similar preferences, we can recommend A the movies that B liked.

🔍 Step 5: Generate Recommendations

A simple recommendation function might look like this:

def recommend(user):
    similar_users = similarity_df[user].sort_values(ascending=False)[1:3].index
    recommended_movies = []
    for u in similar_users:
        top_movies = data[data['User'] == u].sort_values(by='Rating', ascending=False)['Movie'].head(2)
        recommended_movies.extend(top_movies)
    return set(recommended_movies)

Example Output:

print(recommend('A'))
# Output: {'Avatar'}

🧰 Step 6: Visualize Recommendations

Use Power BI or Matplotlib to visualize rating trends and top recommendations.

import matplotlib.pyplot as plt
top_movies = data.groupby('Movie')['Rating'].mean().sort_values(ascending=False)
top_movies.plot(kind='bar')
plt.title('Top Rated Movies')
plt.show()

💡 This helps identify the most popular items and refine your recommendation strategy.

🤖 Step 7: Enhance with ChatGPT or AI APIs

You can make your system smarter by integrating ChatGPT for content-based suggestions.

Example prompt:

“Suggest movies similar to Inception with a focus on sci-fi and thriller genres.”

ChatGPT can generate descriptive tags and summaries that you can include in your dataset for improved matching.

🧠 At AI Computer Classes – Indore, you’ll learn how to blend Python code with AI-powered logic for smarter systems.

🔧 Step 8: Deploy Your Model

Once you’re satisfied with your system:

• Build a simple Flask or Streamlit app for your recommendation interface.
• Connect your data dynamically with Excel or Google Sheets for easy updates.
• Host your app locally or on platforms like Render or Hugging Face Spaces.

This turns your local project into a working, shareable web app!

🧩 Tools You’ll Learn

ToolPurposeBenefitPython (Pandas, Scikit-learn)Core logicBuild algorithms easilyExcel / Power BIData preparationClean and analyze user dataChatGPTAI-driven enhancementAdd smart suggestionsFlask / StreamlitWeb deploymentBuild live apps fast

💡 Why Learn at AI Computer Classes – Indore?

We simplify complex programming concepts into hands-on, real-world projects.

You’ll build your own AI tools using:

• Python fundamentals
• Machine learning workflows
• Power BI dashboards
• API integrations

💬 Perfect for students and professionals aiming to enter the AI, analytics, or software development fields.

🏁 Conclusion

Recommendation systems are everywhere — from shopping sites to learning platforms.

Building one teaches you data handling, algorithm design, and AI fundamentals, all in one project.

Start your journey at AI Computer Classes – Indore, where we turn ideas into skills through practical, guided learning.

📞 Contact AI Computer Classes – Indore

✉ Email: hello@aicomputerclasses.com

📱 Phone: +91 91113 33255

📍 Address: 208, Captain CS Naidu Building, near Greater Kailash Road, opposite School of Excellence For Eye, Opposite Grotto Arcade, Old Palasia, Indore, Madhya Pradesh 452018

🌐 Website: www.aicomputerclasses.com