# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

pip install lightgbm==3.*

pip list

import lightgbm as lgm
train = pd.read_csv("/kaggle/input/mobile-price-classification/train.csv")
test = pd.read_csv("/kaggle/input/mobile-price-classification/test.csv")

train['binary_price_range'] = 0
train.loc[(
    train['price_range'].isin([2,3]),
    "binary_price_range"
)] = 1

X = train[['battery_power', 'blue', 'clock_speed', 'dual_sim', 'fc', 'four_g',
       'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', 'px_height',
       'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time', 'three_g',
       'touch_screen', 'wifi',]]
y = train[["binary_price_range"]]

X_train = lgm.Dataset(X, y)

parameters = {
    "n_estimators": 3,
    "max_depth":3,
    "random_state": 43
}
basic_model = lgm.train(parameters,train_set = X_train)
X['PRED'] = basic_model.predict(X)
df_trees = basic_model.trees_to_dataframe()

df_trees