ROAD ACCIDENT SEVERITY CLASSIFICATION

The primary goal of this project is to build a predictive model that classifies the severity of road accidents into different categories, such as minor, moderate, and severe. This model can assist in identifying high-risk areas and implementing preventive measures to reduce the frequency.

Loan Application app

This application classifies if a person is eligible for loan or not.

Diabetes Prediction

Early detection of diabetes and effective management are crucial for improving the quality of life for those at risk or already diagnosed with diabetes. The project is aimed at developing a predictive model to identify individuals at risk of diabetes based on a range of health-related features.

Twitter Sentiment analysis

By collecting and analyzing tweets mentioning specific Apple and Google products, such as iPhones, MacBooks, Android devices, or Google Workspace, NLP algorithms are employed to classify the sentiment of these tweets as positive, negative, or neutral.

FRAUD DETECTION

The primary objective of this project is to develop a robust and accurate fraud detection system for credit card transactions. The system aims to automatically identify fraudulent transactions, protect cardholders from unauthorized charges.

AI CHATBOT

In this project, we aim to develop a basic chatbot using natural language processing (NLP) techniques and machine learning algorithms. The chatbot will be designed to interact with users, understand their queries, and provide appropriate responses.

Swahili Audio transcription

With the increasing availability of digital audio content in Swahili, AnalytiX Insights aims to develop automated systems that can classify and categorize Swahili audio recordings for various applications, including speech recognition, content recommendation, and language learning tools.

Time series project

Time series data means that data is in a series of particular time periods or intervals. The data is considered in three types: Time series data: A set of observations on the values that a variable takes at different times. Cross-sectional data: Data of one or more variables, Pooled data.

Deployed flower classifier app using streamlit

The Iris Flower Classification project addresses this challenge by employing machine learning algorithms to automate the classification process. The primary goal is to develop a model capable of accurately identifying iris species based on features like sepal length, sepal width, petal length.

Salary prediction based on various features

The problem statement in a salary prediction data science project involves developing a model that accurately predicts salaries based on various factors such as experience, education, location, and skillset.

Unsupervised Techniques PCA and Clustering

The primary challenge revolves around efficiently reducing the dimensionality of high-dimensional data using PCA while preserving its essential information. Subsequently, employing clustering algorithms such as K-means or hierarchical clustering to partition the data into meaningful groups.

PYSPARK PROJECT BIG DATA

Our project revolves around leveraging PySpark, a powerful open-source framework built on Apache Spark, to revolutionize the way we handle data analysis and processing. PySpark, with its Python API, offers unprecedented speed, scalability, and flexibility.

A/B Testing

A/B testing is one of the most important tools for optimizing most things we interact with on our computers, phones and tablets. From website layouts to social media ads and product features, every button, banner and call to action has probably been A/B tested.

House Price Prediction.

The goal of this project is to find correlating factors with the price of a house so that I can make recommendations to the real-estate company based on the findings. Looking for factors that contribute a large amount to the variation of the price of a home so that better business recommendations

Movie Recommendation System.

This project seeks to explore what types of films are currently doing the best at the box office, then translate those findings into actionable insights that can be used to decide what type of films to create.

Image classification using Tensorflow

The Dogs & Cats is a foundational problem for a basic CNN(convolutional neural network) model which involves classifying images as a dog or a cat. The dataset can be used for learning how to develop, evaluate and use convolutional deep learning neural networks for classification of images.

Data Cleaning

Data cleaning refers to the process of identifying and correcting or removing incorrect, incomplete, incorrectly formatted, corrupted, duplicate or irrelevant data within the provided data.

School Management system

A program that enables users to add new students, teachers, schedule classes and so much more

Bank application

Front end and backend