Data Science
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handling-missing-value
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Embark on a transformative "100 Days of Machine Learning" journey. This curated repository guides enthusiasts through a hands-on approach, covering fundamental ML concepts, algorithms, and applications. Each day, engage in theoretical insights, practical coding exercises, and real-world projects. Balance theory with hands-on experience.
reinforcement-learning exploratory-data-analysis models machine-learning-algorithms regression data-acquisition data-visualization feature-extraction supervised-learning classification feature-engineering unsupervised-learning evaluation-metrics clustering-algorithm time-series-analysis handling-missing-value handling-outlier business-problem data-preprocessing-and-cleaning
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The project provides Four Tasks which is given by Cognifyz Technology.
numpy machine-learning-algorithms regression pandas feature-selection classification recommendation-system geographical-analysis preprocessing-data handling-missing-value
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Built a Logistic Regression model in R to predict housing loan approvals using financial and demographic data. Performed data preprocessing, feature engineering, and model evaluation using ROC curve and confusion matrix. Achieved ~74% accuracy, showcasing the effectiveness of ML in loan decision-making.
machine-learning data-analysis logistic-regression confusion-matrix data-preprocessing roc-curve r-studio loan-prediction-analysis training-testing handling-missing-value
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An analysis of house prices in Beijing
time-series time-series-analysis house-price-prediction price-prediction time-series-forecasting house-prices price-analysis house-price-analysis handling-missing-value handling-outlier
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📂 Task's and work completed during my internship at Cognifyz_Technology, focusing on Machine Learning.🧠
numpy machine-learning-algorithms regression pandas feature-selection classification recommendation-system geographical-analysis preprocessing-data handling-missing-value
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This repository contains data analysis programs in the Python programming language.
visualization python neural-network histogram naive-bayes-classifier data-analysis principal-component-analysis apriori-algorithm dbscan-clustering id3-algorithm k-means-clustering comparation association-rule-mining fp-growth-algorithm data-pre j48-classifier handling-missing-value birch-clustering students-acedemic-dataset description-of-code
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Welcome to the FIFA Dataset Data Cleaning and Transformation project! This initiative focuses on refining and enhancing the FIFA dataset to ensure it is well-prepared for in-depth analysis. The project involves a comprehensive data cleaning process and transformation of key features to improve data quality and usability.
python numpy data-transformation pandas feature-engineering data-cleaning fifa one-hot-encoding fifa-dataset clean-dataset handling-missing-value data-cleaning-and-preprocessing handling-duplicate-values prepare-for-analysis
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Exploratory Data Analysis and Data Preprocessing on Marketing dataset. Domain - Retail Marketing
python pandas data-visualization seaborn data-analysis data-cleaning bivariate-analysis univariate-analysis handling-missing-value
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Data Set: House Prices: Advanced Regression Techniques Feature Engineering with 80+ Features
data-science data-transformation missing-values log-transformation label-encoding minmaxscaling handling-missing-value
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This is the curated pile of notebooks/small projects which contains linear and non-linear regression models.
linear-regression eda data-visualization feature-selection scaling feature-engineering pearson-correlation datacleansing nonlinear-regression randomforestregressor handling-missing-value
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End-to-end movie recommendation system using ML, data analysis, NLTK, CountVectorizer, cosine similarity, and TMDB API. Deployed with Streamlit.
python api data-science machine-learning-algorithms web-scraping cosine-similarity data-cleaning dataanalysis handling-missing-value
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Techniques to Explore the Data
outliers-detection handling-missing-value dealing-with-categorical-feature different-types-of-scaling
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This repository contains resources and code examples related to Feature Engineering and Exploratory Data Analysis (EDA) techniques in the field of data science and machine learning.
exploratory-data-analysis eda feature-engineering imbalanced-data feature-scaling data-encoding handling-missing-value data-interpolation handling-outlier
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In this exercise, I'll apply Data cleaning using Handling missing values of San Francisco building permit.
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This repository contains a project focused on data cleaning using SQL, applied to a healthcare dataset.
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Implemented and compared various machine learning algorithms and visualizations on the World Population 2024 dataset to identify the most efficient predictive model. Additionally, evaluated model accuracy using different methods to ensure prediction reliability and precision.
linear-regression exploratory-data-analysis data-visualization decision-tree-regression correlation-analysis random-forest-regression cleaning-data handling-missing-value svr-regression-prediction
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This repository contains pre-requisite notebooks of Data Cleaning work for my internship as a Machine Learning Application Developer at Technocolabs.
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