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Exploratory-Data-Analysis
Here I did EDA on the iris dataset using histograms, scatterplots, probability density function(PDF), cumulative distribution function(CDF), box plots, whisker portsKNN-w-o-sklearn
I made my own KNearestNeighbors algorithm to get better intution of the algorithm which outperformed the scikit-learn's official KNeighborsClassifier's performance getting 98.24% accuracy on Breast Cancer Diagnostic Winscoin dataset.Spam-Classifier
Spam Classifier Spam detection is one of the major applications of Machine Learning in the interwebs today. Pretty much all of the major email service providers have spam detection systems built in and automatically classify such mail as 'Junk Mail'. In this mission we will be using the Naive Bayes algorithm to create a model that can classify dataset SMS messages as spam or not spam, based on the training we give to the model. It is important to have some level of intuition as to what a spammy text message might look like. Usually they have words like 'free', 'win', 'winner', 'cash', 'prize' and the like in them as these texts are designed to catch your eye and in some sense tempt you to open them. Also, spam messages tend to have words written in all capitals and also tend to use a lot of exclamation marks. To the recipient, it is usually pretty straightforward to identify a spam text and our objective here is to train a model to do that for us! Being able to identify spam messages is a binary classification problem as messages are classified as either 'Spam' or 'Not Spam' and nothing else. Also, this is a supervised learning problem, as we will be feeding a labelled dataset into the model, that it can learn from, to make future predictions.MatplotlibTutorials
Here I did some data visualisations with matplotlib.Logistic-Regression-from-scratch
I created a logistic regression from scratch and plotted the decision regions to visualise the decision boundary.Robust-Regression
Here I worked on reducing the effect of outliers in the boston housing data with the RANdom SAmple Consensus(RANSAC) AlgorithmCat-vs-Dog
Here I made my own neural network from scratch during my Neural Networks and Deep Learning course from deeplearning.ai by Andrew Ng. I trained a class vs dog dataset on my neural network and had found accuracy of 80% on evaluation.End-to-end-machine-learning
Here I prepared a basic machine learning project of Housing price prediction, in which I provided all the steps to create a machine learning project from end to end, from reading data, and visualisation to training it and choosing proper hyperparameters.Jamming-with-spotify
MOVIE-RECOMMMENDOR-SYSTEM
Here I made a content based movie recomendor system using basic mathematics and used it to recommend movies based on the ratings of the customer given to the movies..MNIST-DIGIT-CLASSIFIER
Here I made a MNIST digit classifier in which I imported the MNIST dataset and performed all the techniques of machine learning and showed how One Versus One and One Versus All techniques are used for multiclass classification.get-your-food
HSE-Introduction-to-deep-learning
Here are my assignments that I completed in my course Introduction to Deep Learning by coursera from Higher School of Economics on coursera's environment.Titanic-Dataset-EDA
Here I have done Exploratory Data Analysis and Predicted the outcomes manually by visualizing and interpreting from data, without using any machine learning algorithm and technique and got an accuracy of 81.48% which is quite fair.Amazon-Fine-Food-Sentiment
I worked on kaggle kernel on Amazon Fine Food Reviews dataset and applied all the text cleaning, preprocessing and model fitting steps. I cleaned the deduplicated data after which nearly about 69% of original data remained. I applied preprocessor and tokenizer to remove stopwords and emoctions etc.. I used bag of words model and tfidf models to separate out the most useful words. Then I created a pipeline and evaluated the dataset on a logistic regression model to get accuracy of 93.24%%HERE_COMES_THE_CUSTOMER
# Getting Started In this project, we will analyze a dataset containing data on various customers' annual spending amounts (reported in monetary units) of diverse product categories for internal structure. One goal of this project is to best describe the variation in the different types of customers that a wholesale distributor interacts with. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.SENTIclass
Project DescriptionVarianceThreshold
Here I performed feature selection with the help of various methods.Machine-Learning-Techniques
Here I have applied many machine learning regression and classification technique in the Applied Machine Learning course by University of Michigan for understanding.CHARITY-ML
In this project, we will employ several supervised algorithms of your choice to accurately model individuals' income using data collected from the 1994 U.S. Census. We will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Our goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations. Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with. While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features. The dataset for this project originates from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Census+Income).MoodDetector
# IntroductionUdacity-Course-Projects
# Udacity Deep Learning NanodegreeDeepStyle
# Introduction to Neural Style Transferspam-prediction
Animated-Buttons
trivia-card
MyRecommendor
Recommendation Systemhackerearthchallenge
zipfilemyportfolio
# PortfolioSpammer
TV-halftime-shows-and-the-Big-Game
# Project Descriptionblog-palace
soundboard
microsoft-bot
BlogrLandingPage
FlowerClassification
# Flower ClassificationmyCIFAR10Keras
# IntroductionMyLIB
#courseraRESNET-50
# Introductionprofile-card-component
Number-guesser
Apple-Sign-Up-Form
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