Mood Disorders Case

Visit my GitHub to examine the project in detail.

Summary

Scenario

Data exploration for a mood-changing center, the objective of analyzing the following data is to identify patients with mood disorders.

Problem Statement

Medical professionals work hard to improve patients’ lives. The advancement of statistical analysis and machine learning and applying these concepts in the medical field has allowed medical staff to leverage the expertise of data scientists to predict the onsets of diseases and medical emergencies and classify diagnosis.

Objective

Build several prediction models to identify patients who suffer from mood disorders. The goal is to improve patients' lives.

Data

Mood Disorder Dataset. The dataset consists of many features and the target class (Diagnosis).

Part 1 - Data Exploration & Prediction Model with Python

a. Jupyter Notebook using the dataset to complete the following:

* Import Libraries* Load dataset

b. Exploratory Data Analysis

* Remove identifiable features to preserve privacy.
* Data Dimension
* Data Types
* Summary Statistics
* Correlation plots
* Data Distribution (plot features against Target variable)

c. Data Pre-Processing and Wrangling

* Missing Values
* Duplicate Data
* Feature Engineering
* Outliers
* Categorical Data Encoding
* Feature Scaling
* Build functions.

d. Models Building:

* Random Forest
* Extra Tree Classifier

e. Models Evaluation and Comparisons

* K-Fold Cross Validation
* Confusion Matrix
* Accuracy
* Precision
* F1-score

Part 2 - Report Presentation (PPT)

The summarized report shows the uncovered results and potential recommendations to minimize bad outcomes.