Portafolio of data science projects. Using: Python, PyTorch, Tensorflow, Scikit, Keras. Includes Classification, Regression, Time series, NLP, Deep learning, among others.
Tools
- Python: NumPy, Pandas, Seaborn, Matplotlib
- Machine Learning: scikit-learn, TensorFlow, keras
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- Creditcard_fraud_detection:The purpose of this project is Detecting a credit card fraud using logistic regression
- Pima Indians Diabetes Database: The objective of this project is to aid in predicting if a person is likely to have Diabetes or not using the Naive Bayes
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- Iris Dataset using Decision Tree: This consist of using the the decision tree method on the Iris data set to predict the predictor Y or the sepal.length
- covid-19 indian data using Random forest: This project consist of predicting the number of the active, cured or death cases in India using the rondom forest. Also finding the number active, cured or death cases in the Maharastra
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- K -Means clustering on mall customer dataset:K-means clustering is a type of unsupervised learning, which is used when you have unlabeled data (i.e., data without defined categories or groups). The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable K.
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- Dogs vs cats using CNN Neural Network -:The project consists of creating an algorithm to classify whether images contain either a dog or a cat.
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- COVID19 detection using chest x-rays - : Using the proposed AI techniques based workflow we are in a position to analyze thepatient’s condition using x-rays by spending minimal time of radiologists and patient’s money. These sorts of solutions are needed for countries like India where the population size is huge..
- Face recognition using Python -: Recognizing the faces with smile using the open cv2 and python.
- Car Parking Space Counter and Detector: The project consists of detecting car parking space and counting the available space for the car parking using the Open cv2.
- Vehicle detection and color recognition -:This project consists of detecting the car of the color and vehicle detection in the video using background image substractor.
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- Sentiment Analysis on Amazon Reviews -: This project consist of the Analysing the Reviews using the NLTK and carrying the sentiment analysis on the reviews and user rating given by the customers and reviewers.
- Sentiment Analysis on IMDB 50k Movies Reviews -:Sentiments analysis on the IMDB 50K Movies. The consists of finding the best and worst reviews given by the people to the movies.
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- Amazon top 50 selling books - : This project consists of finding the detail of the amazon top 50 books such as price, user ratings and reviews.
- Indian_education_system_EDA - : This project contains information about Indian School Education Statistics of the year 2013-2014 to 2015-2016.The project tells us about the number of the student dropout according to the different state and dropout ratio for the state in the country. At the same time the enrollment ratio of the student for the state in our country
- Uber Data analysis using Python -: This purpose of this projects is to find the difference between the cab rental rates in normal days and in the rainy days.This project gives the better understanding of the cab rental rates on the basis of the weather reports
- Indian_food_EDA -:The purpose of this projects is to find cooking time taken by the dishes after the order is place. This project consists of 255 indian dishes.
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- IPL data -:The purpose of this project is find out the number of the participate in the IPL 2017.The number of the match win and lose by each participating team.Number of the innings played by each participating team in the IPL 2017
- covid -:
- Exploratory Data Analytics on Pima Indians Diabetes Database -:This project consists of Pima Indian Diabetes Database is from National Institute of Diabetes and Digestive and Kidney Diseases. It consists of nine variables, out of which 'outcome' is the target variable. The objective of this project is to aid in predicting if a person is likely to have Diabetes or not.
- Exploratory Data Analysis on Automobile dataset -:This project consists of three types of entities: (a) the specification of an auto in terms of various characteristics (b) its assigned insurance risk rating (c) its normalized losses in use as compared to other cars. The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is more risky (or less), this symbol is adjusted by moving it up (or down) the scale. Actuarians call this process "symboling". A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe.
- Salary_expectation_based_on_experiences -:The purpose of this project is to use data transformation and machine learning to create a model that will predict a salary when given years of experience, job type. ... This model can be used as a guide when determining salaries since it shows reasonable predictions when given information on years of experience, job type
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