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pdivya-mca's Projects

akaike-technologies-assignment

This folder contains the internship assessment for Akaike Technologies, comprising assignments related to Data Science and NLP.

assignment-ml-algorithm

In this repository you can find GUVI Task in Machine Learning. Some sample dataset are Boston Housing Dataset and Car dataset.

blog-question-answer-using-transformer

Blog - Question-Answer (QA) using Transformer is a type of artificial intelligence model that uses the Transformer architecture to find an answer to a question within a given context

credit-card-fraud-detection

Credit card fraud detection is the process of identifying unauthorized use of a credit card. The goal is to predict if a transaction is fraudulent or not.

customer-conversion-prediction

This project aims to predict customer conversion for an insurance company. Using historical data, we will develop a machine learning model that can identify which customers are most likely to purchase a policy. By understanding the patterns and behaviors of previous customers, the model can make predictions and provide valuable insight to company.

eda-on-iris-dataset

Exploratory data analysis (EDA) on the Iris dataset is a process of investigating and summarizing the main characteristics of the data. The Iris dataset is a well-known and widely used dataset in machine learning

exploratory-data-analysis-on-netflix

The goal of an EDA project on Netflix data is to gain insights into the data and understand patterns and trends in the viewership and consumption of content on the platform.

exploratory-data-analysis-on-uber

Exploratory data analysis (EDA) on Uber data is a process of investigating and summarizing the main characteristics of the data.

guvi-codekatta-programs-python

Codekatas typically involve writing code to solve a specific problem, and the goal is to write efficient, working code that meets the requirements of the problem statement.

guvi-codekatta-programs-python-arrays

Codekatas typically involve writing code to solve a specific problem, and the goal is to write efficient, working code that meets the requirements of the problem statement. In this repository you can find the python programs which is related to arrays.

guvi-codekatta-programs-python-mathematics

kaggles-titanic-dataset-data-analysis

In this repository you can find the data cleaning and exploratory data analysis on Kaggle Titanic data set.

logistic-regression-algorithm-classification-algorithm-ml

In this repository you can find the Machine Learning Algorithm for Logistic Regression - Classification Problem. Here we are finding whether person getting admitted in college or not using gmat score, gpa and work experience

machine-learning-algorithms

In this repository you can find the examples of Logistic Regression , K-NN, Decision Tree, Random Forest algorithms.

mongodb_task

This repository is related to Mongo DB task.

multiple-linear-regression_petrol-consumption-dataset

In this repository you can find the ML algorithm for Multiple Linear Regression of Petrol Consumption Dataset

numpy-assignment

This repository is related to numpy task. You can find around 100 questions and answers.

pandas-and-visualizaion

In this repository you can find pandas and visualization assignment given by GUVI.

pandas-task

This repository is related to pandas library. in this you can find around 60 questions with answers.

pattern-programs-using-python

In this repository you can find out the star pattern, number pattern and alphabet pattern programs using python

pdivya-mca

Config files for my GitHub profile.

predict-future-adoption

In this repository you can find the dataset for takehome user. This is the assignment given by GUVI. used EDA and predict future user adoption

registration-and-login-using-python

Created Registration and Login page using python with file handler.

simple-linear-regression-machine-learning-algorithm

Example for simple linear regression model in machine learning with dataset.

spam-email-prediction

Spam email prediction is the process of identifying and classifying unwanted email messages as spam. The goal is to develop a model that can accurately identify spam emails.

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