📌 Learn Machine Learning step by step#
The journey to learn machine learning starts with curiosity and data.
Here’s a simple idea to practice:
🧠 Stock price prediction#
Predicting a stock price is a typical ML project.
It’s a time‑series problem where the data are ordered in time.
🔍 You’ll use historical data to anticipate future values, for example by downloading them from Yahoo Finance.
Common models:
- ARIMA 📈
- LSTM (neural network for sequential data) 🤖
And don’t forget feature engineering:
add lagged values, moving averages, etc., so the model can learn better.
🧾 Sample code (Python)
In this exercise we use a lag or shift():
import pandas as pd
from sklearn.linear_model import LinearRegression
# get data (e.g. from Yahoo)
df = pd.read_csv('AAPL.csv', parse_dates=['Date'], index_col='Date')
df['lag1'] = df['Close'].shift(1)
df = df.dropna()
X = df[['lag1']]
y = df['Close']
model = LinearRegression()
model.fit(X, y)
pred = model.predict(X.tail(1))
print(f'Next prediction: {pred[0]:.2f}')✏️ Brief explanation
Imagine you have a table with a stock’s price for each day.
To predict tomorrow, you tell the model: “look at what happened yesterday” (that’s the lag 1).
The algorithm learns the relationship between yesterday’s price and today’s price, and then uses it to give an approximate number for the next value.
It’s like training someone to recognise patterns in a sequence.
➡️ With this project you practise key concepts:
- 💡 working with time series
- 🔧 creating new columns (features)
- 📊 measuring error with something like MSE
More information at the link 👇
