JudsonMorgan/Planet_study

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JudsonMorgan/Planet_study

JudsonMorgan

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An artificial intelligence algorithms to label satellite image chips with different atmospheric conditions and the different classes of land cover/land use. For this Multi-class Multi-Label problem, some of the labels are from the following categories: Cloud Cover (clear, partly, cloudy, haze), Primary RainForest, Water (rivers, lakes), Habitation (large city, small homes), Agriculture, Roads etc. The algorithms from this project will enable us to understand where, how and why deforestation happens.

Flask_OCR

In this project, we deployed the google tesseract ocr model to flask web framework.

water-quality-prediction-using-ANN

In the project, we first carried out a comparative analysis of ANN and Random forest ensemble classifier. Finally, the ANN model was deployed using Fastapi endpoint as it performs better than the Random forest classifier.

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A-Comparative-Analysis-of-SVM-KNN-and-RandomForest-on-offline-Handwriting-Recognition

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Sms-spam-API

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Ocr_project-flask

An OCR project which extracts texts from images.

House_Price_prediction-Advanced-dataset-

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RedwineQualityPrediction

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Chibueze

Judson

ML_Best_estimator

A few lines of code that could help Machine Learning engineers, select the best machine learning model for any dataset.

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Fake_News_Detection

A project that detects whether a news is fake or not. All thanks to Python and NLTK.

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EDA-Titanic-survival-prediction-using-RandomForestClassifier

A project that predicts whether a passenger survived the Titanic accident or not.

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Effect-of-PCA-on-model-performace

A simple project to illustrate the effect of PCA (pricipal component analysis) on the accuracy score. In this project, we are going to do the following: 1. Load our dataset `heart.csv` data 2. Perform outlier detection and removal on some data features using zcore greater than -3 or less than 3. 3. Standardized our data 4. Building our machine learning model by comparing three different models (SVM, RandomForest, and Logistic Regression) on the same dataset. 5. Now, we apply Dimensionality reduction technique using pca to see how it affect model's performance.

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House_price_predict

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

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Student-Dropout-Prediction-using-ML

Student Dropout Prediction using ML and its visualization functions. To develop a inferential model in order to study the relationship of various psycho-graphic and lifestyle attributes with the grades of a student. Here we apply techniques such as correlation coefficients, data visualization, t-test etc. To develop a classification model that predicts whether a student will drop out or not based on several psycho-graphic and lifestyle attribute.

Jupyter Notebook