50 ML Projects To Strengthen Your Portfolio and Get You Hired

50 ML projects ideas for beginners with source code to practice for your next machine learning engineer job interview to add value and strengthen your portfolio

50 ML Projects To Strengthen Your Portfolio and Get You Hired
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The most trusted way to learn and master the art of machine learning is to practice hands-on projects. Projects help you create a strong foundation of various machine learning algorithms and strengthen your resume. But as the saying goes the voyage of a thousand miles starts with a single footstep, we present to you a 50 first steps guide on your machine learning journey. 


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50 ML Projects Ideas for Beginners with Source Code

We have compiled below a list of 50 machine learning projects that will help you understand the diverse concepts in machine learning. Each project explores new machine learning algorithms, datasets, and business problems. You will have a strong foundation in machine learning and its ways by practicing all these machine learning projects. The ML projects have been classified based on their application across diverse industries for easier understanding. 

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Machine Learning Projects (ML Projects) in Transportation 

ML Project on Demand Prediction of Driver Availability using Multistep Time Series Analysis

The project’s objective is to predict driver availability in a given area using multi-step time series analysis. Usually, food order dropouts occur due to the shortage of drivers and the corresponding surge prices. We can efficiently allocate active drivers to areas likely to experience a lack of drivers with a demand prediction model. Driver Availability is a multi-step time series problem in which we use the prediction of one step as an input of the prediction for the next. The dataset contains three weeks of activity data on each driver like login time, number of hours active each day, date, and driver details like driver gender, age, id, number of kids, etc. There are three data files, namely driver.csv, ping.csv, and test.csv. In this ML project, you will learn to implement the random forest regressor and Xgboost algorithms to train the model.

Demand Prediction for Driver Availability Project with Source Code and Guided Videos

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The projects aim to forecast the ride demand at a given latitude and longitude in the future. The demand prediction is a case of a multi-class time series problem where the forecast is also dependent on the time variable along with other attributes. You will build a model to forecast the area of origin for the future ride requests that will give OLA enough time to respond and allocate drivers to the upcoming ride request and meet the demand.

The dataset set contains ride and user attributes like user id, date and time of ride request, pick and drop locations in longitudes and latitudes. You will learn the implementation of the mini-batch k-means clustering algorithm along with complete autocorrelation function plots. Furthermore, you will learn the concepts of lead and lag in time series in the project. The project uses Random Forest Regressor and XgBoost algorithms with rolling mean and lead-lag values for the highest accuracy. 

OLA Rides Request Demand Forecast Project with Source Code and Guided Videos 

Machine Learning Projects(ML Projects) in Healthcare and Technology

The model aims at classifying human activities that fall under the selected types - laying, walking, sitting, standing, climbing up, climbing down. The dataset for the project was collected by a group of 30 people who went about performing these six activities in a test environment, wearing a smartwatch. All the activities were tracked and video recorded. The built-in accelerometer and gyroscope in the smartwatch measured the 3-axial linear acceleration and 3-axial angular velocity at the constant rate of 50hz in 563 attributes. The data were manually tagged by cross-checking with the recorded video for the experiment. 

Activity Recognition is an example of supervised learning, which uses classification algorithms like decision trees and random forests with accuracy upwards of 90 percent accuracy. The use cases for activity recognition stretch from smartwatches to fitness tracking applications. 

Human Activity Recognition Project with Source Code and Guided Videos

ML Project for Medical Image Segmentation with Deep Learning

This project segments medical colonoscopic images/scans and detects colon polyps present in the frames. It is a computer vision problem that is used extensively in public healthcare systems. The CVC-Clinic dataset contains frames extracted from colonoscopy videos, including many colon polyps frames, taken from Kaggle. You will learn to implement unet++ models for image segmentation using PyTorch. 

Medical Image Segmentation Project with Guided Videos and Source Code 

3. ML Project for Image Segmentation using Masked R-CNN 

You will build a machine learning model to detect fire in images to set up an early fire detection system in public places. The dataset contains twenty fire images for training and ten images for testing. The model will detect fire and smoke pixels in the image and try to identify the fire source. You will learn the basic principles of image processing and normalization in this project. We will also study object localization and image segmentation techniques that form the steps for object detection. This ML project will implement the masked RCNN algorithm, a variant of faster CNN. 

Image Segmentation Project using Masked RCNN with Source Code and Guided Videos 

 ML Project for Ecommerce Product Reviews - Pairwise Ranking and Sentiment Analysis

The project performs sentiment analysis on product reviews and ranks them in order of their relevance. The result of the model would be a list of reviews in descending order of significance. The dataset contains 1600 customer reviews taken from an E-pharmacy portal. The reviews are labeled as informative and not informative for training the classification model. You will learn sentiment analysis and use a Random Forest classifier to classify reviews into useful or not useful. Also, we will use the TF-IDF approach to quantify essential words in the reviews. 

E-commerce Product Reviews Project with Source Code and Guided Videos 

The project objective is to classify and identify plant species using standard classification techniques from binary leaf images. Leaves among plants are unique to each plant and are an excellent metric to classify plants. The dataset contains features like shape, margin, and texture extracted from leaf images. We will use seaborn, NumPy, and sklearn libraries for pixel and greyscale manipulation as well as create various visualizations. You will get to implement popular classifiers like Support Vector Classifier, k-Neighbour Classifier gradient boost classifier. You will also learn to use Linear Discriminant Analysis for model fitting. 

Plant Species Classification Project with Source Code and Guided Videos 

The project aims at classifying the personality of a user into the big-five model of personality. A survey forms the dataset with personal details like hobbies, skills, friend lists, social media activity, etc. Personality assessment is useful when selecting candidates for leadership roles in situations with high stakes and risks. Another application of personality classification is matching people on dating and matrimonial sites.  You will learn the comparison between various algorithms like k-Nearest Neighbours, convolutional neural networks, and linear regression. 

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Machine Learning Projects(ML Projects) in Banking and Finance

The project aims to predict the severity and cost of insurance claims raised by the customer in unforeseen circumstances. Severity in insurance terms means the damage or cost of a catastrophe or event to the insurance company. It helps in analyzing the risk involved in various insurance claims and plans. The Allstate Insurance claims data set contains 116 categorical variables, 14 continuous variables, and a customer id variable. Explicit mentions of categories are avoided as the data is confidential. You will learn to implement the random forest regressor algorithm, and root mean squared value for measuring the model accuracy.

 Allstate Insurance Claim Severity Project with Source Code and Guided Videos

Loan Eligibility prediction is a complex problem that involves many variables and dependencies that influence the decision to grant a loan to the customer. Banks need to assess the probability of customers paying back the loan amount depending on which they issue the loan. The loan is sanctioned if the prediction system shows a green; otherwise, rejected. The dataset used is an anonymized data with more than 1,00,000 loan records that contain fields like customer Id, Credit score, years in the job, annual income, etc. You will learn about H20.ai in python, which offers easy-to-use ML algorithms and the know-how of Gradient Boost Trees and Light Gradient Boost Machines for modeling. You will also get to learn hyperparameters selection using Open SerachCV. 

Loan Eligibility Prediction Project with Source Code and Guided Videos

ML Project on Credit Card Anomaly Detection using Autoencoders

The project detects fraudulent credit card transactions using the available credit card transaction data. Credit card fraud detection is crucial so that the card company can calculate the actual charge from customers and curb fraudulent practices in the future. The dataset contains credit card fraud and legal transactions for some time. We have 28 anonymous attributes as a result of the Principal Component Analysis applied to the original dataset. In addition to that, we have a transaction time and amount field for each unique transaction. You will learn to implement Autoencoders and deep neural networks using the H20 package in R. You will also plot the precision-recall curve to recognize the threshold for the highest accuracy in the model. 

Credit Card Anomaly Detection Project with Source Code and Guided Videos 

The macroeconomics trend project is a slightly different project that aims at predicting the financial movements in world economics to discover new scientifically driven approaches. Simply given many x  variables, we forecast the y variable, which is the financial outcome. Two Sigma provides the dataset on Kaggle for the financial forecasting model. It contains 17,00,000 entries with 109 data attributes divided into three categories - derived features, fundamental features, and technical features. All the features given in the dataset are continuous in values and range. You will learn algorithms like Ridge Regression and Extreme gradient boosting. 

Macroeconomics Trend Prediction Project with Source Code and Guided Videos 

ML Project on Stock Market Forecasting using Time Series Analysis 

The objective of this ML project is to predict stock prices using traditional predictive algorithms and advanced machine learning algorithms. The dataset contains the daily closing prices for European stock indices like DAX, SMI, CAC, and FTSE, with weekends omitted in the timeline. Since stock prediction is a time-series problem, we need to be mindful of underlying trends and correlations in the dataset. You will learn to identify correlations and detrend as well as decompose a time series. The traditional algorithms you will study are the Holt winter model, the ARIMA model, the VAR or Vector Autoregression model, and finally, the novel Neural Networks. 

Stock Market Analysis Project using Time Series with Source Code and Guided Videos 

ML Project on Loan Eligibility Prediction using Gradient Boosting Classifier 

The project classifies loan applications into granted and rejected by studying the user’s credit score and past payment history. The focus is to make the loan application process smooth and streamlined with minor processing at the bank’s end using the classification model. The loan dataset is formed by more than 10,000 loan application records with attributes like customer id, loan amount, loan application date, loan id, credit score, years in current job, etc. You will learn to implement Boosting algorithms and Decision Trees in the project. The best algorithm is chosen using performance measures like the F1 score, ROC curve values, and Mathew correlation coefficient. 

Loan Eligibility Project using GBC With Complete Source code and Guided Videos 

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The project aims to predict the likelihood of a customer to churn and discontinue using the bank’s products and services. The prediction insights help the bank create intervention strategies for customers that are likely to churn. You will implement ensemble learning that combines multiple machine learning models to improve the accuracy of the prediction algorithm. Decision Trees and Linear Regression models will be used in the ensemble technique. The dataset contains 10,000 records and 14 attributes of customer details from the bank. It also has information about services subscribed by the customers with the bank.

Customer Churn Prediction Project with Source Code and Guided Videos

The project aims at predicting whether a person will earn more than 50k per annum or less than the amount given the personal details and educational qualifications of the person. The dataset contains 14 attributes depicting personal details of individuals, a subset of which are as follows - age, Work class, education, relationship status, occupation, etc. We use H20.ai to implement Deep Neural Networks over the cloud. H2O.ai library also provides model validation techniques like the F1 score, which is used to gate-keep the quality and accuracy of the model.  

Census Income Prediction Project with Source code and Guided Videos  

ML Project on Credit Default Prediction

The project’s objective is to predict which a customer is likely to turn into defaulters in the span of the next two years. The idea is to avoid issuing loans to such users and have strict deadlines and actions for delinquency. It helps in minimizing losses for the bank and improving the status among regulatory bodies. The data contains records for 1,50,000 customers who applied for loans, out of which some were unable to pay back the amount. You will learn deep learning implementation with neural networks, logistic regression, and decision trees as a comparison, out of which logistic regression and neural networks perform the best.

Credit Card Default Prediction Project with Source Code and Guided Videos

Machine Learning Projects(ML Projects) in Manufacturing and Retail

Price optimization is the analysis of the relationship between price and sales of different products. It finds the optimal price that will yield the highest profit for the product. Price optimization is used widely across industries like aviation, hotels, banking & finance, and retail. The dataset is made up of three constituent data files - sales, transaction, and date. The sales file contains fields like sell id, sell category, and item id, while the transaction file contains attributes such as price, sell id, sell category. These two data files join using the date file that is common in both to form the complete dataset. We will use linear regression for model building along with many exploratory data analysis techniques to get hands-on experience with price elasticity of demand and optimal prices for maximum revenue. You will also learn the use of visualization libraries like matplotlib and seaborn. 

Source Code and Guided Videos for Retail Price Optimization Project

ML Project for Sales Forecasting using Time Series with Greykite and Neural Prophet

The project takes the historical sales values of the company to forecast future values using the time-series forecasting models. You will learn to forecast sales using the time-series forecasting library called Greykite by python. Sales forecasting is essential for planning the inventory demand and offers beforehand to maximize profit in the future. The dataset for this project is Walmart sales data of 45 Walmart stores located in different regions. It contains files with information related to stores, test data, train data, and features. 

You will learn to use libraries like Greykite along with Neural Prophet to build the model and use RMSE for model validation. You will also learn about trends and seasonality in the time series dataset.

Time Series Sales Prediction Project with Source Code and Guided Videos 

 ML Project for Walmart Sales Forecasting Project in R

This project aims to forecast future sales for a Walmart store given the historical sales data and macro-indicators like the unemployment rate, CPI, fuel price, holidays, etc. Sales Forecasting dictates inventory restocking and staff strength in a store given how good the store is doing and which products are selling the most. These insights can also lead to the opening of new stores in a specific locality or closing one. 

The dataset is taken from 45 Walmart stores daily from many locations. It also contains all the store and product details alongside holiday and offers details as well.

Sales prediction is an example of both time series and prediction problems. You will learn the ARIMA time series model besides traditional prediction models like random forest regressor.

Walmart Sales Prediction Project with Source Code and Guided Videos

The project aims to predict sales for each product at the Bigmart store to plan product availability and restocking accordingly, increasing daily sales and revenue.  The dataset is collected from 1550 Bigmarts stores in 2013 across different locations having distinct attributes like- product MRP, store location, product weight, category, store size and type, etc. You will learn techniques like data scaling and one-hot encoding while implementing this ML project idea. Some standard ML algorithms used in this project for modeling are the Gradient Boost method with MinMaxScaler normalization.

BigMart Sales Prediction Project with Source Code and Guided Videos

Demand Forecasting is the process of predicting future demand for a product by analyzing the present and past trends in market demand. The Kaggle dataset used is from the multinational corporation, Grupo Bimbo with stores in many countries across the globe. It delivers fresh bakery products to 1 million orders annually. The dataset contains 7 billion entries over 11 attributes and details of client, town, and product. 

You will learn to implement algorithms like Support Vector Machine, Gradient Boost Machine, and XgBoost. Additionally, you will explore various feature engineering techniques by introducing moving average and lag features in the dataset. 

Inventory Demand Forecasting Project with Source code and Guided Videos

Rossman is a drug store chain with around 3000 stores in Europe. The project objective is to forecast daily sales of stores six weeks in advance across Germany. Sales prediction helps in the effective planning of inventory and scheduling. The dataset contains sales records from over 1115 stores across many places. The sales forecast depends on attributes such as store location, seasonality, and holidays that make up the dataset. You will master the art of handling outliers and missing values while working on this ML project idea. We will use Stochastic Gradient Descent and Linear Regression to build our prediction model. 

ML Project to Forecast Rossman Stores Sales with Guided Videos and Source Code

ML Project for Similar Image Finder in Python, Keras, and Tensorflow

Build a machine learning model that searches and finds images similar to the given product image. It will be helpful in stores that need to stock similar products next to each other for easy accessibility for customers. The project is an example of computer vision technology that uses Mobilenet architecture. The dataset contains 1,000,000 images of 2019 product categories available for training, out of which about 90,000 images are provided for testing alone. You will learn the use of k-nearest neighbors to find the k-nearest vectors closest to the image in similarity.

Similar Image Finder Project in Python with Source Code and Guided Videos 

ML Project for Customer Market Basket Analysis using Apriori and Fp Growth Algorithms

Market Basket Analysis, also known as Product Association Analysis, is a technique that ascertains the relationship between product purchases. It is the likelihood of a customer buying product B if he has already bought product A. Market Basket Analysis is used by retail stores to group products together at a discount to increase the chance of purchase. The dataset is from the grocery chain FoodMart, which has 325 stores in the US. It contains data files such as customers, products, departments, sales, stores. We join all the data files by the sales data, essential to our modeling. You will learn the Apriori and Fp Growth algorithms and draw a detailed comparison between their accuracies. On a general note, the FpGrowth algorithm performs better than the Apriori algorithm, but the specifics are case-dependent.

Customer Market Basket Analysis Project with Guided Videos and Source Code 

The project aims to predict the average selling price of avocado so that production can be increased to make more profits and cater to more customers. It is a time-series problem, so we need to check for seasonality and trends in the data using heatmaps and visualizations. The dataset for this ML project is taken from Kaggle, representing the actual sales of the Hass avocados. It contains attributes such as avocado type, date of observation, price of avocado, total volume sold, etc. You will learn the implementation of XGB regressor and time-series algorithms like ARIMA and SARIMAX. You will also work with the Facebook Prophet procedure to draw a comparison among all the algorithms used.

Price Prediction using Avacado Dataset Project with Source Code and guided Videos

The project’s objective is to create a model that suggests the optimal selling price to the seller, given the market demand, trend, and seasonality. You will use Mercari, Japan’s most prominent shopping app’s data containing product details like item name, shipping fee, item price, brand name, etc. You will apply different feature engineering techniques such as count vectorize and TF-IDF on the data. You will learn to implement algorithms like support vector machines and random forest models. 

Retail Price Recommendation Project with Source Code and Guided videos 

ML Project on Collaborative Filtering Recommendation System in Python 

The project’s objective is to build a collaborative filtering recommendation system that analyses user likes and dislikes and user interaction as a basis for providing product recommendations. Recommender systems in e-commerce or media streaming services are the best examples of collaborative filtering in action. You will learn about distance matrix and cosine similarity to find users with similar preferences. 

Collaborative Filtering Recommendation Project with Guided Videos and Source Code 

The project objective is to recommend products to Instacart users that they might like after purchasing a particular product. Market Basket is the technique where complementary products are suggested to the user contingent on the already purchased product. It is common knowledge that the purchase of certain products increases the chance of buying other products—for example, purchasing a DSLR camera might lead to a customer buying the lenses used with the camera. The dataset contains 3 million Instacart customer’s transactional records, which have been anonymized for confidentiality. You will learn to implement Apriori and Eclat association rule mining algorithms for modeling the problem and using ggplot for visualizations. 

Instacart Market Basket Analysis Project with Guided Videos and Source Code

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Machine Learning Projects (ML Projects) in Advertising, Media and Entertainment

ML Project for Music Recommendation System with KKbox Dataset 

A music recommendation model suggests users new music based on their preference and listening history. The project’s goal is to predict if a user will listen to a song again in a given period. In essence, it is a prediction problem and uses standard prediction algorithms such as logistic regression, decision trees, and xgboost with ROC curve for model validation. KKBox is among the most prominent music streaming services in Asia, with more than 30 million tracks. The data is compiled in a user-song pair fashion, where for each user-song combination, the time of the first play event is recorded, along with metadata of users and songs. 

Music Recommendation System Project with Source code and Guided Videos

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ML Project on Abstractive Text Summarization using Transformer-BART model

The objective of this ML project is to generate news article headlines by using the Google Transformer-BART model. This is an example of abstractive summarization that creates custom phrases to summarise the input text. It is a prevalent problem in Natural Language Processing  The dataset for this project is taken from a Github repository containing 40,000 professionally written summaries of news articles and the original articles in CSV format. You will learn techniques like tokenization and evaluate the news heading with a BART evaluation matrix which checks the summaries produced by the model against a reference human-written summary.

Abstractive Text Summarization Project with Source Code and Guided Videos

ML Project for Resume Parsing in Python with OCR and Spacy

Resume selection is a cumbersome process when done manually by HR and recruiters. It is incredibly tiring and time-consuming as the format for each resume varies. The resume parsing project aims to simplify the whole process and automate the efforts. It parses the resume, extracts the essential fields, and uses these details to categorize the resumes into specific predefined categories. The dataset for the resume contains information such as candidate name, mailing address, qualifications, graduation year, college, etc. With Spacy’s named entity recognition (NER) technique, you will learn information extraction and optical character recognition with tesseract.

Resume Parsing Project with Source Code and Guided Videos

ML Project on Building a  Chatbot Application using NLTK

The project builds a chatbot using Natural Language Toolkit in python that can reply to queries in natural language and make basic conversations. A chatbot finds use in interfacing with users as a customer service assistant where questions can be raised and resolved. Other usage scenarios include fast food applications, banking, and retail services.  

The dataset for the project is taken from an employee leave inquiry and issue system in an organization. The data is formatted as questions and relevant answers for the question. This project will help you master various NLP techniques like lemmatization, tokenization, a bag of words, etc. You will also get to use Naive Bayes Classifier and Decision tree classifier for modeling. 

ML Project with Source Code and Guided Videos for Chatbot Application using NLTK

The Ad tracking project aims at assessing if a click for an advertisement is authentic or fraudulent. Fraud advertisements divert considerable traffic and revenue from genuine sites to fraudulent ad channels, affecting business and engagement for companies. The dataset contains over 10,000 click records that have click event details from numerous users. A sample set of the click data looks as follows - IP, app, device, os, channel, click time, etc. This ML project uses Smote for data balancing and gives an in-depth understanding of avoiding model overfitting. Random Forest Ensemble model and Logistic Regression give reasonable accuracy for the problem.  

Ad-Tracking Fraud Detection Project with Source Code and Guided Videos

ML Project for Face Recognition System using FaceNet

The goal of this ML project is to identify and classify faces from images and videos. The face recognition model will extract features from the images using OpenCV and Haar Cascade algorithm to learn about the faces it needs to recognize. The approach breaks the images into lines and edges that are used by the Haar Cascade algorithm to build the model. Face recognition finds use in the state of the art technologies like surveillance, face tagging on social media, face-unlock feature on mobile phones, and biometrics. The dataset for the project contains 35 images extracted from the famous TV sitcom show Friends. We have seven images per cast member in the dataset that we use for training. You will learn to implement CNN models and the Facenet system in the process. 

Face Recognition System using Facenet Project with Source Code and Guided Videos

ML Project for Forecasting Business KPIs using TensorFlow

Key Performance Indicators measure a business’s progress against the set objectives. For this ML project, we aim to find the number of times the brand logo appeared in an IPL match between CSK and RCB. Our KPIs for the project are brand logo appearance count, largest and smallest logo area percentages, and logo area per appearance in the video. The dataset is extracted from a 2-minute video of the match from youtube. The youtube video is converted into frames, later annotated into JSON files, and finally formatted into a CSV file with KPI as attributes. You will learn visualization via tensor boards and the concept of CKPT in Tensorflow

Forecasting Business KPIs Project with Source Code and Guided Videos

A look-alike model analyses a small set of users called seed users and finds users with similar characteristics and interests as the seed users. Such a model is used to find a larger audience by sampling a subset of that audience. It is used for growth hacking and improving engagement. The project aims to build a look-alike model that increases the click rate of a social media advertisement using the Locality Sensitive Hashing algorithm or LSHA. The dataset is from an ad campaign that ran on social media and collected important analytics, including user clicks on ads, the total number of users reached, time spent on the ad, etc. You will learn to use the MinHashLSHForest algorithm and generate a larger seed set for the ad campaign. 

Locality Sensitive Hashing ML Project with Source Code and Guided Videos

ML Project on Sequence Classification-Fake News Classification using NLP and Deep Learning

The project uses the Sequence to Sequence deep learning technique to classify unreliable and suspicious news articles as fake. With the vast quantity of fake news after the advent of social media, it is easy to mislead people and influence public opinion. Fake news holds power to sway election votes and results in one’s favor. Thus, it is all the more necessary to curb such instances. The dataset contains attributes like news id, news author, news title, news article text, and fake label to mark the dubious articles as fake. You will learn algorithms like LSTM, Gated Recurrent Units, and simpler RNNs while validating the model with a confusion matrix. You will also use various data pre-processing techniques to clean the dataset, like outlier detection, and finally, use word padding to create sequences out of the vectors. 

Fake News Classification Project with Guided Videos and Source Code

The project aims at building a subjective segmentation model that bundles certain products together on sale. A traditional way to combat such a clustering problem is to use the Market Basket approach and club together recommended products after a product purchase. But the project objective is to solve the problem using time-series clustering. The dataset contains weekly sales transactions over 52 weeks with 800 weekly purchased products. You will learn to implement the k-means clustering algorithm to identify product bundles and calculate the silhouette coefficient/score to measure the goodness of the clustering algorithm you have implemented. After the project, you would also know about Agglomerative clustering and Decisive clustering. 

Project on Identifying Product Bundles with Guided Videos and Source Code

Machine Learning Projects(ML Projects) in Telecommunication

The churn for an organization is the rate at which people unsubscribe from a product or service. It’s a measure to assess the likability of a company, product, or service. We use the Logistic Regression algorithm to model the problem and predict churn for the dataset. Logistic regression works well since it’s a classification problem, and our dataset is linearly dependent. The dataset for this project is taken from a telecom company with information about states, association period with the company, customer service calls, night call charge, total night calls, etc. ROC Curve and Confusion Matrix are used as a performance metric to test the accuracy of the test model

Customer Churn Prediction Project with Source Code and Guided Videos

ML Project for Topic Modelling using K-Means Clustering

Topic modeling is the process of extracting topics from a text that best describes the given text or document. The project finds topics from customer reviews data that explain the reviews succinctly. The dataset for the project is taken from Twitter for the company Vodafone and contains 21000 different customers’ tweets. You will get to learn techniques like vectorization, tokenization, and using regex in python. You will use the K-means clustering machine learning algorithm on the dataset to group similar reviews under clusters.   

Project Source code and Guided Videos for Topic Modelling on Customer Reviews

The project aims to extract a dominant topic from a document capable of annotating the document’s gist. The principle to remember is logically related words are more likely to occur together in a paper than unrelated words. Topic modeling finds use in sifting through unlabelled text or documents and creating sense out of the unstructured text. 

The dataset contains an eclectic collection of 25,000 documents that vary in word length and density along with the dominant topic in the document. For the training model, you will learn Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis ( LSA ) algorithms. Working on this project will give you the know-how of topic modeling metric scores called coherence scores.

LDA Topic Modelling NLP Project with Source Code and Guided Videos  

4. ML Project for Churn Prediction in Telecom using R

The project aims to predict customers likely to churn in the following months and create strategies to avoid the churn. Churn results in loss of revenue for the organization and incurs a bad reputation in the market. The dataset used for this machine learning project is hosted on Kaggle, containing 7000 rows and 21 attributes detailing customer information. You will learn about the Keras Sequential Learning model. Additionally, you will also learn to use pre-processing techniques like one-hot encoding, data scaling, and mean centering. 

Churn Prediction in Telecom Project Source Code and Guided Video

Machine Learning Projects (ML Projects) in Real Estate

ML Project on House Price Prediction using Zillow Dataset

The project builds a predictive model to predict house prices and minimize the difference between actual and estimated prices, called log errors. It is a regression problem helpful in accessing the house price given public data like location, budget, area, etc.  You will learn to use gradient boosting regressor, decision tree regressor for modeling, and mean absolute error as a model validation technique. You will use the Zillow dataset hosted on Kaggle to predict house prices. It contains 60 attributes in total about the house details. Some of the features in the dataset include house location, house selling date, house area in square feet, tax amount, bedrooms, bathrooms, etc.

Zillow House Prediction Project with Source Code and Guided Videos

Machine Learning Projects(ML Projects) in Travel and Hospitality 

Expedia has clustered all its hotels into 100 different groups. These hotel groups have hotels with similar pricing and ratings. This project aims to build a machine learning model to predict whether the customer will make a booking in one of Expedia hotel groups given his browsing history and activity on their website. The data is from customer behavior and interaction with Expedia’s website and search results. It contains attributes that the path a customer took while navigating the site and whether the browsing ended in a successful booking. You will learn to implement Decision Tree Classification and K-Nearest neighbors ML algorithms in this machine learning project.

Expedia Hotel Recommendation Project with Source Code and Guided Videos

ML Project for Digit Recognition using CNN using MNIST Dataset

The project builds a Convolution Neural network to identify handwritten digits and labels them with digital numerals. Manually parsing documents with numerical details like bills and invoices is tiring and a waste of effort and resources. The ML model automates this process and increases the throughput manifolds. The Dataset used is the Modified National Institute of Standard and technology or MNIST, a popular OCR dataset in machine learning. It contains more than 60,000 greyscale handwritten-digits images of size 28x28 pixels. You will learn preprocessing techniques like data scaling, reshaping data, and one-hot encoding. A convolutional neural network model is used for the classification and confusion matrix to evaluate the classification model’s performance. 

Digit Recognition using CNN Project with Source Code and Guided Videos 

The project’s objective is to build an OCR that scans physical documents like invoices and digitally stores the extracted information. We will use Google Tesseract, and the You Only Look Once (YOLO) model to build the machine learning model. Our goal is to extract the invoice number, total amount, and billing date from the invoices. The dataset contains unlabeled invoice images that need to go through the labeling process for use in training. You will use the annotation tool - Labellmg -  to label the photos from the dataset and Pytesseract to extract texts from the image classes. You will also learn to measure model precision using the Mean Average Precision method. 

Python OCR using YOLO and Tesseract Project with Source Code and Guided Videos

ML Project on Wine Quality Prediction in R

The project aims to predict the quality of red wine and analyze the chemical properties that influence the standard of wine. You must try your hands-on on this ML project if you consider yourself a fine wine connoisseur. The dataset contains 1600 rows of wine samples with 11 different technical attributes like wine acidity, residual sugar, chlorides, density, pH value, total sulfur dioxide, etc. We will remove co-dependent variables through feature selection to make the data entries unique and linearly dependent. We will learn to plot analytics using various visualizations graphs and understand the concept of data munging. You will use the Support Vector Machine and Linear Regression algorithms to predict wine quality.

Wine Quality Prediction in R with Source Code and Guided Videos

 

ML Project on Language Translation Model from Scratch

The projects’ objective is to build a real-time machine language translation model capable of translating one language to another and vice versa. Language translation finds application across many industry domains like foreign trade, foreign delegation, media, education, international customer support, etc.

The dataset contains unique sentences from the English language and their corresponding translated French sentences, essentially an English to French translation. There are about 20million sentences stored under text files in the dataset that is hosted on Kaggle. We use word embeddings to understand the relationships between words using word2vec and GloVe packages. You will learn about various data pre-processing techniques like tokenization, sequence padding, and one-hot encoding. Additionally, you will learn the implementation of Recurrent Neural Networks. The model uses the encoder-decoder architecture, wherein the encoder converts the input sequence into a context variable, and the decoder converts the context variable into an output sequence that is translated. 

ML Project to Predict Survival on Titanic 

The project aims at predicting the chance of a person surviving the infamous Titanic incident. The predicting will help us analyze the types and backgrounds of people who were more likely to survive the titanic crash, thus providing insights into the event. The dataset for this ML project contains 891 passenger details like passenger name, age, gender,  socio-economic background, and survival information. Xgboost and gradient boost classifiers provide an accuracy of above 80 percent for predicting the survival rate.

Titanic Crash Survival Prediction Project with Source Code and Guided Videos 

For further reading, look no further, for we have readied a list for you.`

 

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