Ethnicity Detection Model Development

Here’s a structured approach for your Ethnicity Detection Model using pandas, seaborn, and scikit-learn:

1. Data Inspection & Cleaning
python
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import pandas as pd

# Load dataset
df = pd.read_csv("ethnicity_data.csv")

# Inspect dataset
print(df.info()) # Check data types
print(df.describe()) # Summary statistics
print(df.isnull().sum()) # Check for missing values

# Handle missing values (example: filling with the mode for categorical data)
df.fillna(df.mode().iloc[0], inplace=True)

# Check for duplicate rows
df.drop_duplicates(inplace=True)
2. Data Visualization
python
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import seaborn as sns
import matplotlib.pyplot as plt

# Ethnicity distribution
sns.countplot(x="ethnicity", data=df)
plt.title("Ethnicity Distribution")
plt.show()

# Pairplot to visualize relationships between features
sns.pairplot(df, hue="ethnicity")
plt.show()

# Correlation heatmap (if numeric features are available)
sns.heatmap(df.corr(), annot=True, cmap="coolwarm")
plt.show()
3. Train-Test Split & Encoding
python
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from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder

# Encode categorical target variable 'ethnicity'
le = LabelEncoder()
df["ethnicity"] = le.fit_transform(df["ethnicity"])

# Select features and target
X = df.drop("ethnicity", axis=1)
y = df["ethnicity"]

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
4. Model Training (Random Forest Classification)
python
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from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# Initialize and train the model
rf = RandomForestClassifier(n_estimators=100, random_state=42)
rf.fit(X_train, y_train)

# Predictions
y_pred = rf.predict(X_test)

# Accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f"Model Accuracy: {accuracy:.2f}")
5. Confusion Matrix
python
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from sklearn.metrics import confusion_matrix
import seaborn as sns

# Confusion Matrix
cm = confusion_matrix(y_test, y_pred)
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues")
plt.xlabel("Predicted")
plt.ylabel("Actual")
plt.show()
6. Accuracy Improvement
Feature Engineering: Create new features based on domain knowledge (e.g., demographic features like age, location).

Hyperparameter Tuning: Use GridSearchCV or RandomizedSearchCV for model optimization.

Class Imbalance Handling: If classes are imbalanced, use techniques like SMOTE or class weights to balance the model.

Boosting Models: Consider XGBoost or LightGBM for higher accuracy.

Would you like to explore hyperparameter tuning or methods for handling class imbalance?