Repository containing portfolio of data science projects completed for academic, self learning, and professional purposes. Presented in the form of Jupyter Notebooks.
Tools
- Python: NumPy, Pandas, Seaborn, Matplotlib
- Machine Learning: scikit-learn, TensorFlow, keras
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- K-Nearest Neighbors - Social Network Ads Dataset: Using K-NN on customers that bought a SUV from a social network ad.
- Artificial Neural Network (ANN): I have created ANN by adding input layer, multiple hidden layer and output layer then compile the ANN and fitting the ANN to training set and later predicting the test set result.
- Machine Learning Regression - Financial Market: Importing from quandl (financial and economical data) to create a simple regression.
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Finding_Outliers by mean and SD): I have created ANN by adding input layer, multiple hidden layer and output layer then compile the ANN and fitting the ANN to training set and later
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SkipGrams - StringExample_removing white space - TextSpecificWords - TokenizeInputFromFile
- Tokenizing - Word2Vec - removeWhitespace - stopwords : Extracting Features from Data and Transforming Text into Vector and Algorithms used Bag-Of-Words,Word2Vect and Skip-Gram (Reducing Noise) e.t.c
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- Artificial Neural Network (ANN): I have created ANN by adding input layer, multiple hidden layer and output layer then compile the ANN and fitting the ANN to training set and later
- Exploratory Data Analysis - Admission Predict by Descriptive_Bivariate_Statistic: Simple analysis of Admission Predict including quick visualizations with correlation plots and heat maps.
- BRAST CANCER Detection: Predicting if the cancer diagnosis is benign or malignant based on several observation/features using SVM with quick visualizations with correlation plots and heat maps Later Evaluate the model and improve the model.
- Exploratory Data Analysis - House Prices: Simple analysis of house prices including quick visualizations with correlation plots and heat maps.
- Simple Linear Regression: Small playground to summarize and study relationships between two continuous variables from a randomized dataset.
- Exploratory Data Analysis - Univariate Statistic: Simple analysis of ‘Brain_data’ including quick visualizations.
- Exploratory Data Analysis - Titanic Passenger Information: Simple analysis of passengers on board the Titanic answering common questions with visualizations.
- Random_python_practice: Practice has done for any data science project from data preparation to Visualization. From simple python - Data Frame Visualization.
If you enjoyed what you saw, want to have a chat with me about the portfolio, work opportunities, or collaboration, feel free to contact me on: - LinkedIn