Ish muhitini sozlash

python --version

mkdir ml-loyiham
cd ml-loyiham

# Muhit yaratish
python -m venv muhit

# Faollashtirish - Windows
muhit\Scripts\activate

# Faollashtirish - macOS, Linux
source muhit/bin/activate

# Chiqish
deactivate

pip install numpy pandas matplotlib seaborn scikit-learn jupyter

# Tekshirish
pip list

jupyter notebook

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn

print("NumPy:       ", np.__version__)
print("Pandas:      ", pd.__version__)
print("scikit-learn:", sklearn.__version__)

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

# Sun'iy ma'lumot yaratamiz: uy maydoni va narxi
np.random.seed(42)

maydon = np.random.uniform(40, 150, 50).reshape(-1, 1)
narx = maydon.flatten() * 8 + np.random.normal(0, 60, 50) + 100

# Modelni o'qitamiz
model = LinearRegression()
model.fit(maydon, narx)

bashorat = model.predict(maydon)

print(f"Koeffitsiyent: {model.coef_[0]:.2f}")
print(f"Kesishma:      {model.intercept_:.2f}")
print(f"Aniqlik (R2):  {model.score(maydon, narx):.3f}")

plt.figure(figsize=(8, 5))
plt.scatter(maydon, narx, alpha=0.7, label="Haqiqiy narxlar")
plt.plot(maydon, bashorat, color="red", linewidth=2, label="Model bashorati")
plt.xlabel("Maydon (m2)")
plt.ylabel("Narx (mln so'm)")
plt.title("Uy maydoni va narxi bog'liqligi")
plt.legend()
plt.grid(alpha=0.3)
plt.show()

ml-loyiham/
├── muhit/                  # virtual muhit (Git ga kirmaydi)
├── malumotlar/
│   ├── xom/                # o'zgartirilmagan asl fayllar
│   └── tayyor/             # tozalangan ma'lumot
├── daftarlar/
│   ├── 01-tahlil.ipynb
│   └── 02-model.ipynb
├── manba/
│   ├── tayyorlash.py
│   └── model.py
├── modellar/               # saqlangan modellar
├── .gitignore
├── requirements.txt
└── README.md

import pandas as pd

# Barcha ustunlarni ko'rsatish
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)

# Kasr sonlarni chiroyli ko'rsatish
pd.set_option('display.float_format', '{:.2f}'.format)

# Grafiklarni daftar ichida ko'rsatish (eski versiyalarda kerak)
%matplotlib inline

# Ogohlantirishlarni yashirish
import warnings
warnings.filterwarnings('ignore')