Meghna Rawat's repositories
KPMG-Data-Analytics-Virtual-Internship
In this online program, I completed similar tasks that KPMG Graduates do in the company. I learned what it is like working at one of the world’s best data analytics team, and built skills required to excel as a analytics consultant.
Walmart-Sales-Forecasting-using-Best-ML-algorithms
The objective of this project is to develop a Walmart sales forecasting model using the best machine learning algorithms. It also aims to provide insights into the dataset and conclude with reliable sales forecasts for Walmart.
50_Startups_Dataset
This repository is especially made for beginners and has clear descriptions to understand the code to analyze the dataset and see the result.
Batman_Wordcloud
Creating a Wordcloud based on the script of the movie 'Batman' with Python
BestPricedApartment_YouTube
A repository for the YouTube tutorial on finding the best-priced apartment using Simple Linear Regression and Python
Flight-Booking-Price-Prediction
The project is to analyze the flight booking dataset obtained from a platform which is used to book flight tickets.
Handwritten-Alphabet-Recognition-Using-EMNIST-Dataset
About Handwritten Alphabet & Digit Recognition Web Application
Phishing-Websites-Detection
Build a model to find out if a website is Legitimate/Suspicious/Malicious using different algorithms. We will find out which model fits our data perfectly and detects the correct phishing website, making precision and accuracy to be high.
Predicting-Visitor-Purchases-with-a-Classification-Model-with-BigQuery-ML
I have used ecommerce data from the real Google Ecommerce storefront: https://shop.googlemerchandisestore.com/ Firstly, I loaded their visitor and order data into BigQuery and then built a machine learning model to predict whether a visitor will return for more purchases later.
Task2-IIEC-
Task 2 of IIEC Rise
Titanic-Survival-Prediction
In this repository, we will go through the whole process of creating a machine learning model on the famous Titanic dataset. We have two .csv files for the training and testing of data so as to make predictions. Initially, we began with analyzing our training data and checking for any missing data and found out which features are the most significant for making better predictions. During this whole process, we used seaborn and matplotlib libraries to perform the visualizations. During the data preprocessing part, we computed missing values, converted features into numeric ones, grouped values into categories and created a few new features. Afterwards we started training 9 different machine learning models, picked one of them (random forest) and applied cross validation on it. Then we discussed how random forest works, took a look at the importance it assigns to the different features and tuned it’s performace through optimizing it’s hyperparameter values. Lastly, we looked at it’s confusion matrix and computed the models precision, recall and f-score.
Twitter-Sentiment-Analysis-and-Text-Analytics
Classifying whether tweets are hatred-related tweets or not using CountVectorizer and Support Vector Classifier in Python