Age Detection Model Development

Here’s a structured approach for your Age 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("age_dataset.csv")

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

# Handle missing values (example: filling with median values)
df.fillna(df.median(), inplace=True)

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

# Age distribution
sns.histplot(df["age"], bins=20, kde=True)
plt.show()

# Pairplot for relationships (if multiple features exist)
sns.pairplot(df, hue="age")
plt.show()
3. Train-Test Split & Encoding
python
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from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

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

# Scale numerical features (important for models like SVM, Neural Networks)
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Split data
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
4. Model Training (Random Forest)
python
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from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error

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

# Predictions
y_pred = rf.predict(X_test)

# Model Evaluation
mae = mean_absolute_error(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)
print(f"MAE: {mae:.2f}, MSE: {mse:.2f}")
5. Confusion Matrix (for Classification-based Age Groups)
If age prediction is treated as a classification problem (e.g., age groups like "Child", "Teen", "Adult"), you can use a confusion matrix:

python
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from sklearn.metrics import confusion_matrix

# Convert ages to categories (example: 0-12: Child, 13-19: Teen, etc.)
df["age_group"] = pd.cut(df["age"], bins=[0, 12, 19, 35, 60, 100], labels=["Child", "Teen", "Young Adult", "Adult", "Senior"])

# Encode categorical labels
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df["age_group"] = le.fit_transform(df["age_group"])

# Train model again with age groups
X = df.drop(["age", "age_group"], axis=1)
y = df["age_group"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)
rf_classifier.fit(X_train, y_train)

y_pred_class = rf_classifier.predict(X_test)

# Confusion Matrix
cm = confusion_matrix(y_test, y_pred_class)
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues")
plt.xlabel("Predicted")
plt.ylabel("Actual")
plt.show()
6. Accuracy Improvement
Feature Engineering: Try adding derived features (e.g., facial landmarks if using images, behavior-based patterns).

Hyperparameter Tuning: Use GridSearchCV or RandomizedSearchCV to optimize the model.

Boosting Methods: Consider Gradient Boosting (XGBoost, LightGBM) for better accuracy.

Increase Training Data: More diverse data improves model generalization.

Do you want to predict exact age (regression) or age group (classification)?