BasketBall Throws throughout time: Descriptive analysis using NUMPY, PANDAS and SEABORN
Finances Analysis: Data Analysis and Feature Engineering with lists using loops.
World Trends Dataset: Descriptive analysis using the main packages from python (NUMPY, PANDAS MATPLOTLIB)
Titanic Machine Learning Project: Classification Algorithm
Novel-COVID-19 in Portugal:
- (i) ARIMA TimeSeries Model Prediction
Area Find Coordinates: Create a method to find the area of a damaged component in a PCB using Shapely.Geometry Package
Iris Machine Learning Project:
- (i) SVM Kernel = linear
- (ii) Decision Trees
- (iii) Logistic Regression
ICT Blocks Analysis: EDA Descriptive analysis and statistics regarding test system blocking in manufacturing
Pedidos de Ajuda ICT: EDA Descriptive analysis and statistics regarding test system aid in manufacturing
Pokemon stats data analysis: EDA Drescriptive statistics on Pokemon Dataset regarding Speed, Atack, Defense, Sp. Defense, Sp. Attack
WW2 Weather Machine Learning Project: Predict Min.Temp
- (i) Linear Regression CVS
- (ii) RMSE and Robust Scaler
Fish Machine Learning Project:
- (i) Data Scaling and Normalization
- (ii) Linear Regression
- (iii) Gradient Descent
- (iiii) Predicting new inputs
Wine Quality Machine Learning Project.
- (i) Normality Test to all Features
- (ii) Outliers Removal Function by Quartile 25/75
- (iii) Feature Selection by Chi2 and Anova HT
- (iiii) Random Forest Classifier (GridSearchCV)
- (iiiii) AUC Curve and LogLoss Metric
- (iiiiii) Saving model using Pickle package
Rain in Australia Machine Learning Project.
- (i) Exploratory Data Analysis
- (ii) Deal with Null Values by relationship/heamap, removing, ffoward/backfoward, mode, median
- (iii) Feature Selection by Anova H.Testing
- (iiii) SVC Modeling
- (iiiii) Training the data into Neural Networks using Keras API
- (iiiiii) 2 Hidden Layers - No Dropout - Relu Activation and Binary CrossEntropy
- (iiiiiii) Saving Keras Model
Voids Data Analysis - Checking Normality
- (i) QQPlot
- (ii) Histogram/Distplot
- (iii) Shapiro Wilk Normality Test
- (iiii) Kolmogorov-Smirnov Normality Test
Weather Predictive Model - Apply ML to forecast weather
- (i) Data Gathering & ELT (Fillna)
- (ii) Remove columns without relationship
- (iii) Apply KNN Model to Train Data & Test Data
- (iiii) Flask Web Service Input Form Data Gathering
- (iiiii) Apply inputs into ML model, obtain Forecast
- (iiiiii) Flask provide with answer to User