My Projects

Projects Data Analytics

Projects 1
Melbourne House Price Segmentation

The purpose of this project is to analyze the factors that affect property prices in Melbourne and can be used as consideration in making decisions or strategies. More details

Projects 2
Heart Failure Prediction

A simple process of regression heart failure case using Random Forest and Logistic Regression. The purpose of this project is to help prediction heart failure using clinical features. More details

Projects 2
Disney+ Dashboard

Dashboard visualization to describe the trend of movies and tv shows released by Disney+ from 1930-2021 equipped with a comparison of each type rating, top directors and the number of titles around the world. More details

Projects 1

pairplot
Melbourne House Price Segmentation

Overview :
In this repository you will see simple process of price segmentation property real estate in Melbourne, Australia using K-Means algorithm. Dataset used in this segmentation call Melbourne Housing Snapshot from kaggle

Process:

  1. Load Dataset
  2. Checking null values using method info()
  3. Handling missing values using modus for categorical values and mean for numerical values
  4. Exploratory Data Analysis(EDA) to understand and get insight form dataset
  5. From EDA process will be decided features/input for prediction using K-Means
  6. Using K-Means prediction with n_cluster = 3. Number of n_cluster is get from elbow method
  7. Using method describe() to find minimum and maximum values from each features
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Projects 2

heatmap1 heatmap2
Heart Failure Prediction

Overview :
Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Four out of 5CVD deaths are due to heart attacks and strokes, and one-third of these deaths occur prematurely in people under 70 years of age. Dataset used from kaggle

Process:

  1. Did checking missing value
  2. Did exploratory data analysis to get insight about data
  3. Transform categorical value using LabelEncoder()
  4. Train and testing dataset
  5. Using RandomForest() and LogisticRegression() algorithm
  6. Make a heatmap from confusion matrix to know True Label and Predicted Label from result prediction
View

Projects 3

dashboard
Disney+ Dashboard

Overview :
Dashboard visualization to describe the trend of movies and tv shows released by Disney+ from 1930-2021 equipped with a comparison of each type rating, top directors and the number of titles around the world. Dataset used from kaggle.

Detail Sheet:

  1. Movies or TV Shows Worlwide
  2. Amount Movies/TV Shows by Type Rating
  3. Top 10 Directors by Total Release Film
  4. Trend of Movies/TV Shows per Year
  5. List Movies/TV Shows
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