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NikhilaThota / CapstoneProject_House_Prices_Prediction

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Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and suggested a best model to predict the final sale price of a house. Seaborn is used to plot graphs and scikit learn package is used for statistical analysis.

python machine-learning correlation linear-regression cross-validation data-visualization data-extraction data-analysis regularization standardization datawrangling predictive-modeling ridge-regression data-exploration k-fold lasso-regression encoding-library parameter-tuning root-mean-squared-error-metric regression-analysis
  • Updated Jan 19, 2018
  • Jupyter Notebook

martandsingh / dataanalytics

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Python Data Analytics, Machine Learning & Natural Language Processing

machine-learning natural-language-processing sentiment-analysis pandas-dataframe sklearn pandas credit-card-fraud spacy datawrangling movie-reviews nlp-machine-learning classification-algorithm alogrithms algorithms-and-data-structures breastcancer-classification dataanalytics sklearn-library codemaker loan-prediction-analysis dataanalysisandmlusingpython
  • Updated Apr 4, 2020
  • Jupyter Notebook

ShubhendraKumar / Complete-Chefs-Data-Science

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Repository containing self built case studies and analysis leveraging most of the phases in the entire executable data science chain end to end, ranging from harvesting of resources to building cutting edge deep dive data models.

json collection nosql metrics exploratory-data-analysis evaluation inference feature-selection statistical-analysis documents datawrangling feature-engineering datamining statistical-models datascraping dataanalysis exploratory-data-visualizations datacleaning machienlearning
  • Updated Nov 23, 2019
  • Jupyter Notebook

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