Little Bits of Artificial Intelligence

A. Basic Level – Getting Started

1. Linear Regression

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_regression

# Generate sample data
X, y = make_regression(n_samples=100, n_features=1, noise=10, random_state=42)

# Split into training and testing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# Model creation
model = LinearRegression()
model.fit(X_train, y_train)

# Prediction
y_pred = model.predict(X_test)

# Output
print("Coefficient:", model.coef_)
print("Intercept:", model.intercept_)

Concepts: Model fitting, training-test split, coefficients.

2. Classification using k-NN

from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier

# Load dataset
data = load_iris()
X, y = data.data, data.target

# Train model
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X, y)

# Predict
print("Prediction:", knn.predict([[5.1, 3.5, 1.4, 0.2]]))

Concepts: Supervised learning, classification, k-nearest neighbors.

B. Intermediate Level – Real Data and Pipelines

3. Data Preprocessing + Logistic Regression

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.metrics import classification_report

# Load real dataset
df = pd.read_csv("https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanic.csv")
df = df[['age', 'fare', 'survived']].dropna()

X = df[['age', 'fare']]
y = df['survived']

# Split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

# Create pipeline
pipe = make_pipeline(StandardScaler(), LogisticRegression())
pipe.fit(X_train, y_train)

# Evaluate
print(classification_report(y_test, pipe.predict(X_test)))

Concepts: Pipelines, scaling, real-world datasets, classification metrics.

4. GridSearchCV for Hyperparameter Tuning

from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV

params = {'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf']}

# SVM + Grid Search
grid = GridSearchCV(SVC(), params, cv=5)
grid.fit(X_train, y_train)

print("Best Parameters:", grid.best_params_)
print("Best Score:", grid.best_score_)

Concepts: Model tuning, grid search, cross-validation.

C. Advanced Level – Model Stacking and Feature Engineering

5. Feature Selection with RandomForest

from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectFromModel

# Fit a random forest
rf = RandomForestClassifier(n_estimators=100)
rf.fit(X_train, y_train)

# Select features based on importance
selector = SelectFromModel(rf, threshold="median")
X_new = selector.transform(X_train)

print("Reduced feature shape:", X_new.shape)

Concepts: Feature selection, model-based filtering.

6. Model Stacking with VotingClassifier

from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier

# Create multiple classifiers
clf1 = LogisticRegression()
clf2 = DecisionTreeClassifier()
clf3 = SVC(probability=True)

# Combine with voting
ensemble = VotingClassifier(estimators=[
    ('lr', clf1), ('dt', clf2), ('svc', clf3)], voting='soft')

ensemble.fit(X_train, y_train)
print("Ensemble Accuracy:", ensemble.score(X_test, y_test))

Concepts: Ensemble learning, soft voting, model fusion.

7. Pipeline with Imputation + Scaling + Model

from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler
from sklearn.ensemble import GradientBoostingClassifier

from sklearn.pipeline import Pipeline

pipeline = Pipeline([
    ('imputer', SimpleImputer(strategy='mean')),
    ('scaler', MinMaxScaler()),
    ('classifier', GradientBoostingClassifier())
])

pipeline.fit(X_train, y_train)
print("Accuracy:", pipeline.score(X_test, y_test))

Concepts: Full pipeline with missing value handling.

Next – FNN Neuron in Hidden Layer