Sukonmi/Streamlit-Checkpoint-2
Domain:
socioeconomicRecord type:
project
Creator:
Suk
Host:
In this checkpoint, I worked on the "Financial Inclusion in Africa" dataset that was provided as part of the Financial Inclusion in Africa hosted by the [Zindi](Zindi Africa (Competition P…) platform.
# Financial Inclusion in Africa - ML Prediction App
# 🎯 What You're Aiming For
This project demonstrates how to predict which individuals are most likely to have or use a bank account using the 'Financial Inclusion in Africa' dataset.
### ➡️ Dataset Link
Dataset Link
### ➡️ Columns Explanation
Columns Explanation Link.
# ℹ️ Instructions
1. **Install the necessary packages**:
2. **Import your data and perform basic data exploration phase**:
3. **Display general information about the dataset**:
4. **Create a pandas profiling report to gain insights into the dataset**:
5. **Handle Missing and corrupted values**:
6. **Remove duplicates, if they exist**:
7. **Handle outliers, if they exist**:
- Implement your strategy to handle outliers
8. **Encode categorical features**:
9. **Based on the previous data exploration, train and test a machine learning classifier**:
10. **Create a Streamlit application (locally) and add input fields for your features and a validation button at the end of the form**:
11. **Deploy your application on Streamlit share**:
- Create a GitHub and a Streamlit Share account.
- Create a new git repo.
- Upload your local code to the newly created git repo.
- Log in to your Streamlit account and deploy your application from the git repo.
### Example Code
```python
pip install pandas scikit-learn streamlit
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
data = pd.read_csv('path_to_your_dataset.csv')
print(data.head())
print(data.info())
le = LabelEncoder()
data['encoded_column'] = le.fit_transform(data['categorical_column'])
features = data.drop('target_column', axis=1)
label = data['target_column']
x_train, x_test, y_train, y_test = train_test_split(features, label, test_size=0.2, random_state=42)
model = RandomForestClassifier()
model.fit(X_train, y_train)
print("Model trained successfully")
```