Kshitij Sharma's Projects
A typical anomaly detection task and performing KMeans, PCA, Gaussian distribution, and Isolation Forest.
Udacity-->arcade-game
Friendly Cars is a dealership that offers new cars from a single manufacturer. The dealership is located in a suburb of a large city. Its gross sales exceed one million dollars per year. It has ten employees – Jim Friendly (the owner/manager), eight salespeople, and an office manager.
CityBuddy is an MTA-based navigation app designed for both New New Yorkers and Native New Yorkers. It prioritizes accessibility and language support, and features personalized alerts, hard-coded maps for underground travel, and skill-dependent directions. Community-enhanced data ensures accurate information.
ClassicSolitaireUnity reimagines the timeless Solitaire game with Unity, featuring immersive gameplay, customizable themes, and adaptive difficulty. Ideal for enthusiasts and newcomers alike, it modernizes the classic card game for all ages
CS691 Wiki Templates
Edureka
On Demand Home Services offers you all the home services that you may need, such as maintenance, cleaning, personal services, etc..
In this python notebook, analysis of IPL matches from 2008 to 2020 is done using python packages like pandas, matplotlib and seaborn.
Udacity-->feed-reader
👐 Hands-on experience with 😺 Hashcat along with deep dive in 🔐 password rules, hashing, and password cracking ⚠
Front-End Web Development
This problem is a typical Classification Machine Learning task. Building various classifiers by using the following Machine Learning models: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), XGBoost (XGB), Light GBM and Support Vector Machines with RBF kernel.
In this i tried to design a model that can predict the price of stock using different methods and algorithms.
We are trying to design a model that can predict the price of stock using different methods and algorithms.
Applied the K-Means algorithm on the Iris dataset, and utilized the Silhouette Score method to find the best value of K
Kaizntree-Full-Stack Take Home Challenge
GitHub profile.
Leetcode Blind 75 problems solved in Python for interview prep.
Leetcode NeetCode 150 problems solved in Python for interview prep.
Leetcode NeetCode All problems solved in Python for interview prep.
Architecting a low-level system to improve performance and modularity! 🚗
Udacity-->memory-game
Udacity-->myreads_a_book_tracking_app
Udacity-->neighborhood_map_react
Individual Conditional Expectation (ICE) plots display one line per instance that shows how the instance's prediction changes when a feature changes. The Partial Dependence Plot (PDP) for the average effect of a feature is a global method because it does not focus on specific instances, but on an overall average.
The goal of SHAP is to explain the prediction of an instance x by computing the contribution of each feature to the prediction. The SHAP explanation method computes Shapley values from coalitional game theory. The feature values of a data instance act as players in a coalition.