Python solutions to solve practical business problems.
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A series looking at implementing python solutions to solve practical business problems. Share your own projects on this subreddit, r/datascienceproject. Every week we will look at hand picked businenss solutions. See the following google drive for all the code and github for all the data. If you follow the LinkedIn page, you would be able to see the lastest developments.
Outlier Analysis, Model Selection, Missing Values, Descriptive Statistics
Process Text, pyLDAvis, Word Embeddings, Text Evaluation, fuzzywuzzy
RFM Analysis, Pareto Model, NDB Model, Gamma-Gamma Model, CLV Model, Constraint Programming
Radar, Silhouette, PCA, Grouping, Invoices, Inventory, Datatable, Basket,
Week, EDA, Simulated, Prediction, Dummy Variable
Neural Network, Sales, Relu, LSTM, CNN, Evaluation
Full Pipeline, Random Forest, Visualisation, Grid Search, Confidence Interval
Efficient Frontier, Stocks, Modern Portfolio Theory, Pivot, Simulations, Minimum Volatility, Sharpe Ratio
GDP, Life Satisfaction, Linear Regression Plots, Prediction Model
Default, Credit Scores, Visualisations, Data Cleaning, ROC Curves, Multi-class Classification
Capital Allocation, Decision Trees, Acquisitions, Investment
Voting Classifiers, Bagging Ensembles, SMOTE, XGBoost, Cross-validation
OSEMN, Bagging Ensembles, AUC, Model Comparison, ROC Graph, Feature Importance Graph