• Data Science is the process of extracting useful insights from data.
• Uses statistics + programming + domain knowledge.
• Common steps: collect → clean → analyze → visualize → model → deploy.
• Tools: Python, NumPy, Pandas, Matplotlib, Scikit-learn.

• Visualization helps understand patterns and trends.
• Common charts: line, bar, scatter, histogram, box plot.
• Libraries: matplotlib and pandas plotting.

import matplotlib.pyplot as plt

x = [1, 2, 3, 4]
y = [10, 15, 12, 20]

plt.plot(x, y)
plt.title("Line Plot")
plt.xlabel("X")
plt.ylabel("Y")
plt.show()

• Machine Learning (ML) enables systems to learn from data and make predictions.
• Types:
• Supervised (labeled data): regression, classification
• Unsupervised (unlabeled): clustering
• Reinforcement: reward-based learning
• Common terms: features (X), label/target (y), model, training, testing.

• Linear Regression predicts a continuous value.
• It fits a best line: y = mx + c
• Used for predicting price, marks, sales, etc.

# Example: predict marks based on study hours
# y = m*x + c

• Load dataset using read_csv(), inspect using head(), info().
• Check missing values using isna().sum().

import pandas as pd

df = pd.read_csv("data.csv")

print(df.head())
print(df.info())
print(df.isna().sum())

• Split dataset into training and testing sets.
• Training data is used to learn model parameters.
• Testing data is used to check model performance.

from sklearn.model_selection import train_test_split

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

• Steps: import → create model → fit → predict → evaluate
• Library: scikit-learn

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# Example dataset
df = pd.DataFrame({
    "hours": [1, 2, 3, 4, 5],
    "marks": [35, 40, 50, 60, 65]
})

X = df[["hours"]]
y = df["marks"]

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

model = LinearRegression()
model.fit(X_train, y_train)

pred = model.predict(X_test)

print("Predictions:", pred)
print("MSE:", mean_squared_error(y_test, pred))
print("R2:", r2_score(y_test, pred))

• 1) Load dataset (Pandas)
• 2) Clean data (missing values, duplicates)
• 3) Select features (X) and target (y)
• 4) Split dataset (train/test)
• 5) Train model (fit)
• 6) Predict and evaluate