Beginner Friendly Guide | From Zero to Results
1. Basic Python Words You Need to Know
| Word | Meaning | Simple Explanation |
|---|---|---|
pandas |
Data Library | Like Excel inside Python. Used to open and work with tables. |
pd |
Nickname for pandas | Short name so we don’t type “pandas” every time. |
df |
DataFrame | Your table / spreadsheet in Python. |
read_csv() |
Open CSV file | Loads your city-weather.csv file. |
2. Complete Code (Copy-Paste in Jupyter)
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import confusion_matrix, accuracy_score
import seaborn as sns
import matplotlib.pyplot as plt
# Load data
df = pd.read_csv('city-weather.csv')
# Prepare data
data = df.copy()
le = LabelEncoder()
data['city_name'] = le.fit_transform(data['city_name'])
data['weather_category'] = le.fit_transform(data['weather_category'])
# Features and Target
X = data.drop(['weather_category', 'date'], axis=1)
y = data['weather_category']
# Split and Train
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
model = DecisionTreeClassifier(criterion='entropy', max_depth=6, random_state=42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print("Accuracy:", round(accuracy_score(y_test, y_pred)*100, 2), "%")
print("Confusion Matrix:")
print(confusion_matrix(y_test, y_pred))
3. What is a Decision Tree?
A Decision Tree is like a game of 20 Questions. The computer asks yes/no questions about temperature, humidity, wind, etc., and finally predicts the weather category.
4. Results Meaning
- Accuracy: How many predictions were correct (e.g., 85% = very good for this kind of data).
- Confusion Matrix: Shows how many times the model was right or wrong for each weather type.
Tip: Run the code step by step in Jupyter Notebook. Start small and understand each part.
Done with KNIME, Excel, SQL, PowerBI — now learning Python too! Great job! 🚀