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internshala machine learning assignment solution

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Customer Churn Prediction: A Bank wants to take care of customer retention for its product: savings accounts. The bank wants you to identify customers likely to churn balances below the minimum balance. You have the customers information such as age, gender, demographics along with their transactions with the bank. Your task as a data scientist would be to predict the propensity to churn for each customer. Data Dictionary There are multiple variables in the dataset which can be cleanly divided into 3 categories: I. Demographic information about customers • customer_id - Customer id • vintage - Vintage of the customer with the bank in a number of days • age - Age of customer • gender - Gender of customer • dependents - Number of dependents • occupation - Occupation of the customer • city - City of the customer (anonymized) II. Customer Bank Relationship • customer_nw_category - Net worth of customer (3: Low 2: Medium 1: High) • branch_code - Branch Code for a customer account • days_since_last_transaction - No of Days Since Last Credit in Last 1 year III. Transactional Information • current_balance - Balance as of today • previous_month_end_balance - End of Month Balance of previous month • average_monthly_balance_prevQ - Average monthly balances (AMB) in Previous Quarter • average_monthly_balance_prevQ2 - Average monthly balances (AMB) in previous to the previous quarter • current_month_credit - Total Credit Amount current month • previous_month_credit - Total Credit Amount previous month • current_month_debit - Total Debit Amount current month • previous_month_debit - Total Debit Amount previous month • current_month_balance - Average Balance of current month • previous_month_balance - Average Balance of previous month • churn - Average balance of customer falls below minimum balance in the next quarter (1/0)

(Note: In the same downloaded folder, you can find the dataset (churn_prediction) for this problem statement. Once you upload the final project, you will be able to download the project solution (Final project solution.zip). This folder contains the Jupyter notebook file that contains the solution to this final project problem statement.)

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Home » Data Science » Machine Learning Projects

12 Machine Learning Projects with Source Code [2024]

Machine learning is a field of artificial intelligence that enables computers to learn from data and make decisions without being explicitly programmed. Machine Learning projects are practical applications of machine learning algorithms that help solve real-world problems. They are used in a variety of industries, including predictive analytics, natural language processing, and picture and audio recognition.

The current advancements in machine learning include reinforcement learning, adversarial learning, and the growth of ethical concerns in the deployment of machine learning models. In this blog, we will go through some extensive examples of machine learning projects with source code.

Table of Contents

What are Machine Learning Projects?

Machine Learning is based on the deep learning algorithm. It uses the appropriate computing of the deep learning algorithm’s hidden layer, input layer, and output layer to process the data equally. It also simulates the drill and operation of data using the output data text. Supervised learning is the process of adjusting the parameters of a classifier to attain the desired performance using a set of samples from known classifications.

Machine learning project ideas use machine learning models to solve real-world problems or automate operations. These projects often entail data collection, cleaning and pre-processing, the selection of relevant machine learning algorithms , and the training and evaluation of the models.

There are numerous types of projects on machine learning, ranging from beginner-level projects involving the development of simple models to sophisticated projects including the use of cutting-edge techniques and massive datasets. One can gain expertise in machine learning through machine learning courses and training.

Machine Learning Projects For Beginners

Coming up with topics for ML projects can be hard. These projects should be interesting and unique for the examiner. To help you with that, here are some project ideas for machine-learning apps and websites.

1. Weather Forecasting

A project in machine learning can be particularly useful for weather forecasting. Its goal will be to build a model that can predict the weather based on the relevant data of a specific location.  The following are the steps to build this project:

• Data Collection: The candidate can collect data on the location that they want to use in their project. It includes tracking daily weather conditions, like humidity, temperature, precipitation, and other relevant data.
• Feature Selection: The candidate must select the most relevant features that can help predict the weather. It includes statistical analysis techniques to transform raw data into meaningful features.
• Select Model: The candidate should choose a machine learning algorithm that is appropriate for the data and problem. Common algorithms that are used for weather forecasting include support vector machines and neural networks.
• Train Model: The candidate should then train the machine learning model. It involves splitting the data into training and testing sets and evaluating the performance of the model on the testing data.
• Deployment: It is the final step of the model development. Once the candidate is satisfied with the performance, they can deploy the project.

Review the Weather Forecasting Project Source Code .

2. Food Delivery Time Prediction

Predicting food delivery time is a common problem in the food industry. Hence, students can work on this problem as one of their projects.

The following are the steps involved in building a food delivery time prediction model:

• Data Collection: The candidate can collect data about food orders, including the restaurant location, order time, customer location, order type, and order items.
• Feature Selection: The candidate must select the most relevant features that can help with food delivery. The features that can be useful include the time of the day, the distance between the restaurant and the customer, and the number of items in the order.
• Select Model: The candidate should choose a machine learning algorithm that is appropriate for the data and problem. Common algorithms that are used include linear regression and neural networks.
• Train Model: The candidate should then train the machine learning model. It involves splitting the data into training and testing sets and evaluating the model’s performance on the testing data.
• Deployment: The model training is followed by a final step of the model development, i.e., once the candidate is satisfied with the performance, they can deploy the project.

Review the Food Delivery Time Prediction Project Source Code .

Machine Learning Projects for Final Year Students

There are many machine learning-based projects that you can choose from in your final year. But you have to keep in mind that the project you pick should reflect your previous years of learning. The following are some of the project ideas for final-year students.

3. Loan Eligibility Prediction

The loan eligibility prediction can be a great and unique project. The candidate can use historical data of borrowers to train a machine learning model that can predict whether a new applicant is eligible for a loan or not.

The following are the steps that must be followed to build a loan eligibility prediction model:

• Collect Data: The candidate should gather the data required for training the model. It should include the details of past borrowers like their income, loan amount, loan term, and loan status.
• Select Feature: The candidate should then identify the relevant features that have a significant impact on loan eligibility.
• Select Model: After that, an appropriate model should be chosen by a machine learning algorithm such as logistic regression, or decision tree.
• Deployment: After the selected model is tested, the candidate should deploy it to a web server for an idea of how it works.

Review the Loan Eligibility Prediction Project Source Code .

4. Coupon Purchase Prediction Project

The coupon purchase prediction project would be a great application of machine learning in the e-commerce domain. In this project, the candidate can use historical data of customer transactions to train a machine-learning model that can predict whether a customer is likely to purchase a coupon or not. 

The steps to develop this model remain the same as the prior projects, however, the data collection method changes. The following are the steps:

• Collect Data: The candidate should gather the data required for training the model. It includes the details of past customer transactions and other relevant features.
• Select Feature: Relevant features should be engineered from the data that can help the model make accurate predictions. 
• Select Model: The candidate must choose an appropriate machine learning algorithm such as logistic regression or gradient boosting.
• Deployment: After the selected model is tested, it should be deployed to a web server for real-time predictions.

Review the Coupon Purchase Prediction Project Source Code .

Machine Learning Projects for Professionals

Here are some machine learning project ideas for professionals.

5. Detection of Fake News

This project can be really helpful in the detection of sources that spread fake news. If there is a slight chance that the news might be fake or it might not give out the right information to the audience, you can get to know about its authenticity through machine learning models which take the help of tools such as Naive Bayes, Support Vector Machines (SVMs), etc. 

The following are the steps to build a fake news detection project:

• Collect Data: The candidate should collect a large dataset of news articles, including fake ones. There is a variety of datasets available for fake news detection to use.
• Extract Feature: A feature that can be used by the machine learning model should be extracted from text data. Techniques like n-grams and part-of-speech tagging can be used.
• Select Model: The candidate should choose a suitable machine-learning algorithm for this task. Some popular algorithms include logistic regression and neural networks.
• Deployment: Once the selected model is tested, it should be deployed to check if it can work for what it was made.

Review the Detection of Fake News Project Source Code .

6. Similar Image Finder

Let’s say that you come across a pair of goggles that you like and you remember seeing a similar pair elsewhere. All you have to do is click a picture of that product and the machine learning application will automatically show you pictures that look similar to the picture that you clicked. Accordingly, based on the results, you will be taken to various shopping sites from where you can purchase the same product.

The following are the steps to build this project:

• Image Preprocessing: For this step, the input images are preprocessed to resize, crop, or normalized to a standard size or format.
• Extract Feature: The candidate extracts key features from the images, which include texture, shapes, or other visual elements.
• Measurement: Then the similarity between different image features is measured to determine how closely they match. 
• Retrieval: After the similarity is measured, the algorithm retrieves the closest matches to the query image.

Review the Similar Image Finder Project Source Code .

7. Email Spam-Filtering System

The email spam filtering project is part of the text classification problem, which seeks to automatically classify a given text document into predefined categories or classes. A piece of text (such as an email, news article, customer review, tweet, etc.) is the input to the text classification system. The output is the category or labels that best describe the content of the text. 

The objective is to create a machine-learning model that can classify emails as spam or not. To accomplish this, the model is trained on a labeled email dataset, and new emails are then classified as spam or not using the trained model.

To build this machine learning project out of the other project ideas, computation techniques for text classification are used, such as sentiment analysis, text summarization, topic classification, machine translation, etc.

Text processing, text sequencing, model selection, and implementation are a few of the crucial steps in developing this kind of machine-learning project. Use libraries, like Sklearn, NumPy, Counter, Scrubadub, Beautifier, and Seaborn and machine-learning frameworks, like TensorFlow and Keras. You can also use the Spambase dataset for training the model.

Review the Email Spam-Filtering System Project Source Code .

8. Recommender System Project

Machine learning models are available in many applications, allowing for recommendations or suggestions based on prior search history. The objective of recommendation engines is to display suggestions for increased user engagement on a specific platform. Have you noticed that the app starts suggesting series or movies to watch based on what you have previously watched? Just like media and entertainment applications, giant e-commerce platforms, such as Amazon, Flipkart, eBay, etc., have similar features. 

To build this project, libraries are used for testing and developing recommendation models for your machine learning project. For example, recommended lab, ggplot, data.table, reshape2, etc. are libraries that you can use. To build recommendation engines using machine learning models, it is preferable to use datasets like Google Local, Amazon product reviews, MovieLens, Goodreads, NES, and LibraryThing. These datasets are a well-researched compilation of information on prices, categories, ratings, reviews, timestamps, and customer preferences.

Review the Recommender System Project Source Code . 

9. Store Sales Forecasting

Having quantitative awareness about the sales demand for every product present in an inventory is essential information for giant B2C retailers like Walmart, IKEA, Big Basket, etc. It helps to predict the rise or fall in sales for a particular product. This type of prediction regarding sales demand is called sales forecasting. It helps in reducing waste in the production of goods and making judicious use of the budget. 

To build such projects, implementing different approaches to cleaning raw data is very much needed. A thorough understanding of regression analysis, particularly simple linear regression, is essential for developing machine learning projects. Libraries like Dora, Scrubadub, Pandas, and NumPy are useful to model a sales forecasting project for a retailer. 

Review the Store Sales Forecasting Project Source Code .

10. Credit Card Fraud Detection Project 

Machine learning has made it easy to detect credit card fraud. This kind of machine-learning project can distinguish fraudulent credit card transactions from legitimate and genuine ones. The important component of this project is to perform a thorough and accurate classification of the data. 

To build this project, you will need familiarity with machine-learning concepts like decision trees, logistic regression, gradient-boosting classifiers, and artificial neural networks. Using libraries like NumPy, Pandas, Matplotlib, Seaborn, XGBClassifier, and frameworks like Scikit-Learn, you can implement the credit card fraud detection project. Training the machine-learning model requires the use of credit card datasets and credit-card fraud detection datasets. 

Review the Credit Card Fraud Detection Project Source Code .

11. Sign Language Recognition

While some people have to use sign language to communicate because of their special conditions, it is not easy to comprehend for others. With the help of machine learning, a lot of progress has been made in technology to help individuals who cannot hear (are deaf) or speak (are dumb). 

To build this project, the first requirement is to understand all the concepts of sign language, as this project will help in detecting and understanding the signs. Concepts like natural language processing, data prediction, and computer vision (CV) will be used for this project. 

Use libraries like NumPy, OpenCV, SimpleITK, etc., and model frameworks like Keras and TensorFlow. The dataset to train the model varies from country to country as the sign language differs everywhere. It is better to use a collection of sign language recognition datasets from various countries. The machine-learning algorithm can train over a large set of signs using this dataset of images.

Review the Sign Language Recognition Project Source Code .

12. Recognition of Emotions Through Speech

Machine learning is used in this project to identify and interpret the emotions of the speaker. It primarily works with audio data. It will take the user’s speech as input and deduce the user’s emotions as output.

To build this project, Python libraries such as NumPy, Soundfile, Pyaudio, and Librosa are going to be used. Librosa is a special Python library that helps analyze audio data and music files. Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) will be used as the dataset to train the model. The dataset includes 7356 voice samples from 24 professional actors, training a machine-learning model with various speech intensities like happy, calm, angry, upset, surprised, fear, awful expressions, and emotions. The dataset includes songs with similar emotional levels, allowing for a diverse understanding of human emotions.

Review the Recognition of Emotions Through Speech Project Source Code .

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Advantages of Machine Learning

Machine learning is an extremely exciting branch of computer science that can predict outcomes based on previous data or the behavior of the user. The following are the advantages of machine learning. 

• It helps with the recognition of images.
• It assists the users with speech recognition.
• When it comes to predictions, it can even predict diseases and health issues in a person. 
• It helps personalize suggestions on social media and other apps, making browsing more accurately defined and helpful for the user. 
• It also helps with business market predictions. 

Machine learning projects are an important aspect of the areas of data science and artificial intelligence. Machine learning models are used in these projects to solve real-world issues, automate processes, and predict future occurrences. Various machine-learning initiatives can have a substantial influence on society, ranging from image identification and natural language processing to predictive modeling and anomaly detection. While these initiatives might be difficult, they also provide excellent chances for learning, creativity, and making a positive change in the world.

If you are a beginner, you can do weather forecasting and food delivery time prediction machine learning projects. For the advanced level, you can choose projects such as fake news detection and similar image finder.

To do a self-project in machine learning, first, identify a real-world problem you want to solve with your project. The next steps will involve data collection, cleaning and pre-processing, the selection of relevant machine learning algorithms, and the training and evaluation of the models.

To do an ML project in Python, you have to first install Python and the SciPy platform. The next steps will involve loading the dataset, summarizing, and visualizing it. Next, evaluate and select machine learning algorithms. Finally, make predictions and evaluate them.

While Python is the most popular programming language for machine learning, you can work with other programming languages, such as R, Java, Scala, and Julia as well. However, Python is considered more suitable because of its easy implementation, built-in tools, readability, etc.

If you are familiar with the Python programming fundamentals, then learning Python for machine learning is not hard. However, understanding machine learning algorithms can be difficult for beginners due to their complexity.

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Praveen Kumar is the Senior Data Scientist of Internshala. He is an analytics savant who has won national analytics competitions. With more than four years of experience in analytics and Data Science, he is a master of many domains including Python, MySQL, Google Analytics, R, PowerBI, Google Data Studio, NLP, and Machine Learning.

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