Shubham Choudhary's repositories
movie-recommender-system
The movie recommender system collects data from various sources, such as user ratings, movie reviews, and user profiles. The data is then preprocessed to clean and transform it into a format that is suitable for analysis. The algorithm then analyzes the data to make predictions about the movies that a user is likely to enjoy.
digits_classifier
classify the Digits using Random Forest Classifier.
Fake-News-Prediction
This project is based on Machine Learning . For this i used the Decision Tree Algorithm and some comman libraries like numpy,pandas and sklearn .The main aim of this project is to predict weather a News is fake or not and dataset which i used have a labelled which we taken from kaggle
Hospital-management-using-flask-framework
Project Statement: Hospital Management System is a tool which can be used to store, retrieve information about the different patients,procedure etc. Not only it stores information but also provides different methods to retrieve information. For e.g., getting the details of a patient(his procedures, address,date, phone number, etc.). This makes the retrieval of information quick, which not only saves time but also helps in smooth functioning of the hospital. Introduction: Hospital Management System handles the basic management of the patient and doctor database, for the smooth functioning of the hospital, helping them to retrieve information easily and adding new information to the database. FrontEnd: HTML, CSS, BootStrap BackEnd: Flask, Mysql(Database), Python
XGboost
XGBoost is a scalable and highly accurate implementation of gradient boosting that pushes the limits of computing power for boosted tree algorithms, being built largely for energizing machine learning model performance and computational speed.
univariate-bivariate-multivariate
Univariate analysis looks at one variable, Bivariate analysis looks at two variables and their relationship. Multivariate analysis looks at more than two variables and their relationship.
Steps-in-Machine-Learning
#steps in machine learning #1.Import data often comes with csv file #2.clean the data (remove missing or duplicate values)# EDA #3.split the dataset into 2 sets training set and testing set #e.g if we have 1000 pictures of cats and dogs we can reserve 80% for training and 20% for testing #4.create a model #This involves selecting an algorithm to analyze the data.so many mahine learning algorithms out there such as decision tress #random forest,neural networks etc. #each algorithm has pros and cons in terms of acuuracy and performance. #Libraries out there that provides algorithms.one of most popular library is scikit-learn. #so build a model using these algorithms #5.Train the model #method .fit(Training data) #we feed the training data to the model.model will learn the patterns in the data . #6.make predictions #.predict(Testing data) #eg.we ask the model is it a cat or dog if we come with a new image from the testing dataset and our model is then make #predictions.predictions are not always accurate. #7.Evaluate the predictions #in this step, evaluate the predictions and measure their accuracy.if accuracy is low,then #we need to get back to our model and either select a different algorithm that is going to produce a more accurate result for #the kind of problem we are solving or fine tune the parameters of oue model. #each algorithm has parameters(Hyperparameters) that we can modify to optimize the acccuracy.