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explanatory-analysis-of-automobile-dataset

Used data science Techniques especially data visualization, data preprocessing and feature engineering then apply machine learning algorithms to predict future trends.

finding_a_better_place_in_scarborough-_toronto

loan-prediction-classifier

Build a classifier to predict whether a loan case will be paid off or not.

machine_learning_algorithms

Implementation of Machine_Learninng Algorithms in simplest way.

searching-methods

Searching Algorithms are designed to check for an element or retrieve an element from any data structure where it is stored. Based on the type of search operation, these algorithms are generally classified into two categories:1-Sequential Search: In this, the list or array is traversed sequentially and every element is checked(Linear Search),2-Interval Search: These algorithms are specifically designed for searching in sorted data-structures. These types of search algorithms are much more efficient than Linear Search as they repeatedly target the center of the search structure and divide the search space in half (Binary Search).

stock-market-price-prediction

Stock Market Price Prediction: Used machine learning algorithms such as Linear Regression, Logistics Regression, Naive Bayes, K Nearest Neighbor, Support Vector Machine, Decision Tree, and Random Forest to identify which algorithm gives better results. Used Neural Networks such as Auto ARIMA, Prophet(Time-Series), and LSTM(Long Term-Short Memory) then compare make Inferences about the model.

wine_quality-prediction

Machine Learning model to predict the quality of red and white wine based on its chemical properties.