<jupyter_start><jupyter_text>Regression AnalysisNotebook that showcases the required steps to explore and manipulate the data  Import Libraries<jupyter_code>%load_ext autoreload
%autoreload 2

import pandas as pd
import numpy as np
import sys
sys.path.append("../src")

# Data Processing
import pandas as pd
import numpy as np

# Model
import ts_plots, ts_models

pd.set_option('display.max_rows', 500)<jupyter_output><empty_output><jupyter_text>Data Load<jupyter_code>data = pd.read_parquet("../data/02_processed_data/df_full.parquet")<jupyter_output><empty_output><jupyter_text>Feature Selection  Own Prods<jupyter_code>y_col = 'CUB Prod 1 Share (%) of total beer'
cols = [x for x in data.columns if 'CUB Prod' in x and 'Price' in x]+[y_col]
y = data[y_col]

x_cols = [x for x in cols if y_col not in x]
X = data[x_cols]
df_res = ts_models.search_best_combination(y,X)
ts_plots.plot_scatter_matrix(X,trim_label = 14,height=800,weight=800)<jupyter_output><empty_output><jupyter_text>Comp Prods<jupyter_code>cols = [x for x in data.columns if 'Comp Prod' in x and 'Price' in x]+[y_col]

x_cols = [x for x in cols if y_col not in x]
X = data[x_cols]
df_res = ts_models.search_best_combination(y,X)
ts_plots.plot_scatter_matrix(data,trim_label = 11,height=900,weight=900)<jupyter_output><empty_output>
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