kshitijshrivastava1903

Amazon_HackOn_2021_AmazeSafe

Developed an iOS app that fetches data from API calls at regular intervals of time, from a Nodemcu, which is connected to a box in which Customer's Amazon package will be there. The Nodemcu will send the threat alert to the app, which will push notification to the user, on the basis of vibrations, or if the WiFi is disconnected, if someone unauthorized picks up the box, ensuring its safety. The app can also make post API calls to the Nodemcu to sanitise, or open the box. All this will ensure the safety of the box, and customers will no longer have to present at home to pick up their package

Blackjack_game_python

This is the code for the famous blackjack game i created using python's 'random' library and object oriented programming concepts. It contains the hit and stay options we can use and the dealer is the computer. We can have a virtual bank and bet whatever amount of money from it, we want

Classifying-cancer-tumour-data-as-malignant-or-benign-using-PCA-and-Logistic-regression

Built a 97% accurate logistic regression model on breat cancer dataset by reducing the dimensions of the data using Pricipal Component Analysis and applying logistic regression on the reduced 2 principal components, to accurately classify data. Used numpy, pandas, matplotlib, PCA, and scikit-learn.

E-Commerce-iOS-App

iOS Ecommerce App connected with firebase, that lets sellers add items, their details, like images, price, description, and lets users as customers add their items to a cart in sync with their email ID through firebase.

Insurance_Premium_Prediction_ml_project

Used 5 regression algorithms, linear regression, polynomial regression, ridge regression, xgboost regression and neural network regression to find the best ML model to predict medical expenses of a person on the basis of features like age, sex, bmi, region, children.

Intel_Image_Classification_Challenge

Built and trained my own convolutional neural network and 4 other pre-trained CNNs (Resnet-50, VGG-16, Inception-V3, Xception) on the dataset of images provided by Intel, to predict whether a given image has buildings, forests, sea, streets, mountains or glaciers.

Liver-Disease-Prediction-Using-ML_Algorithms

Used 5 different supervised machine learning algorithms and trained them with real data of people with and without liver disease. Then evaluated the results of each of them using different parameters to choose the best one.

Movie-Recommedation-System-Using-Python

Built a movie recommendation sytem, using real datasets of movies titles and thier ratings given by 2.5 million people. Used elements of data creation and modification like numpy, pandas, pivot tables and statistical functions like corrwith() to find similar movies.

Sentiment-Classification-Movie-Review-LSTM-NLP

Trained a bidirectional LSTM Recurrent Neural Network on around 38,000 movie review texts to recognise whether a given movie review is positive or negative and output the corresponding sentiment score between 0 and 1.

Song-recommendation-Using-AI-and-Digital-Signal-Processing

This is a song recommendation system, that uses deep learning, digital signal processing and content based recommendation algorithm to recommend songs similar to a given song, by classifying its genre and converting each song to a genre vector.

kshitijshrivastava1903/Liver-Disease-Prediction-Using-ML_Algorithms

Used 5 different supervised machine learning algorithms and trained them with real data of people with and without liver disease. Then evaluated the results of each of them using different parameters to choose the best one.

kshitijshrivastava1903/Song-recommendation-Using-AI-and-Digital-Signal-Processing

This is a song recommendation system, that uses deep learning, digital signal processing and content based recommendation algorithm to recommend songs similar to a given song, by classifying its genre and converting each song to a genre vector.

kshitijshrivastava1903/Blackjack_game_python

This is the code for the famous blackjack game i created using python's 'random' library and object oriented programming concepts. It contains the hit and stay options we can use and the dealer is the computer. We can have a virtual bank and bet whatever amount of money from it, we want

kshitijshrivastava1903/E-Commerce-iOS-App

iOS Ecommerce App connected with firebase, that lets sellers add items, their details, like images, price, description, and lets users as customers add their items to a cart in sync with their email ID through firebase.

kshitijshrivastava1903/Insurance_Premium_Prediction_ml_project

Used 5 regression algorithms, linear regression, polynomial regression, ridge regression, xgboost regression and neural network regression to find the best ML model to predict medical expenses of a person on the basis of features like age, sex, bmi, region, children.

kshitijshrivastava1903/Intel_Image_Classification_Challenge

Built and trained my own convolutional neural network and 4 other pre-trained CNNs (Resnet-50, VGG-16, Inception-V3, Xception) on the dataset of images provided by Intel, to predict whether a given image has buildings, forests, sea, streets, mountains or glaciers.

kshitijshrivastava1903/Movie-Recommedation-System-Using-Python

Built a movie recommendation sytem, using real datasets of movies titles and thier ratings given by 2.5 million people. Used elements of data creation and modification like numpy, pandas, pivot tables and statistical functions like corrwith() to find similar movies.

kshitijshrivastava1903/Sentiment-Classification-Movie-Review-LSTM-NLP

Trained a bidirectional LSTM Recurrent Neural Network on around 38,000 movie review texts to recognise whether a given movie review is positive or negative and output the corresponding sentiment score between 0 and 1.

kshitijshrivastava1903/Classifying-cancer-tumour-data-as-malignant-or-benign-using-PCA-and-Logistic-regression

Built a 97% accurate logistic regression model on breat cancer dataset by reducing the dimensions of the data using Pricipal Component Analysis and applying logistic regression on the reduced 2 principal components, to accurately classify data. Used numpy, pandas, matplotlib, PCA, and scikit-learn.

kshitijshrivastava1903/Data-cleaning-analysis-Logistic-regression-and-Principal-Component-Ananlysis-on-Titanic-dataset

Used the titanic dataset, cleaned it for null values and categorical features, applied logistic regression and then reduced data to its two principal components and again applied logistic regression to check for accuracy of predicting whether or not a person survived the titanic crash on the basis of data.

kshitijshrivastava1903/Data-Science-And-Machine-Learning-Projects

Used supervised and unsupervised machine learning algorithms for classification and regression tasks on real datasets

kshitijshrivastava1903/Neural_network_analysis_on_Lending_Club-Dataset

Used tensorflow's neural network model to predict whether or not a person pays back a loan on the basis of his historical data and personal details of 3.9 lakh people like interest rate, employment details, address, etc.

kshitijshrivastava1903/Tic_tac_toe_2_player_python_code

This is a 2 player game I have created using python(using concepts of functions and loops)of tic tac toe in which you can enter the the number on the grid to specify where you want to put the X or 0

kshitijshrivastava1903/Amazon_HackOn_2021_AmazeSafe

Developed an iOS app that fetches data from API calls at regular intervals of time, from a Nodemcu, which is connected to a box in which Customer's Amazon package will be there. The Nodemcu will send the threat alert to the app, which will push notification to the user, on the basis of vibrations, or if the WiFi is disconnected, if someone unauthorized picks up the box, ensuring its safety. The app can also make post API calls to the Nodemcu to sanitise, or open the box. All this will ensure the safety of the box, and customers will no longer have to present at home to pick up their package

kshitijshrivastava1903/css_practice

kshitijshrivastava1903/Facial_Expression_Recognition_OpenCV

Trained a convolutional neural network to recognise 7 kinds of emotions (Happy, Anger, Sad, Fear, Disgust, Surprise, Contempt) after detection of face in real time video camera and giving the prediction.

kshitijshrivastava1903/firebase_quiz_app

kshitijshrivastava1903/food_landing_page

kshitijshrivastava1903/Investment-Calculator

kshitijshrivastava1903/kshitijshrivastava1903

kshitijshrivastava1903/Open-contributions

This Repository is for Learning purpose, and open contributions under DevIncept program.

kshitijshrivastava1903/Personal-Website

kshitijshrivastava1903/Tic-Tac-Toe_Project

kshitijshrivastava1903/Web_Development_Projects