Correlation feature selection followed by regression

Question

I have quarterly results data for a company with around 100 variables. Total 60 quarters results are available (total records 60).

sample data: (only few columns & 10 rows)

I would like know following,

1. which ML algorithms / methods to be used to get the information about most important variable(s) affecting the price movement

2. how much each variable is contributing towards price movement (+ve or -ve)

3. predict, if a variable value is changed then how it'll affect the price movement

Thanks!

Answer 1

I think this is a case for linear regression with a lasso/ridge penalty. The lasso/ridge does „shrink“ features/variables, so that it is easy to see which features are important. Since you have 100 variables, you could opt for lasso, since lasso can also „automatically“ exclude features. Here is a lasso example in Python: https://datascience.stackexchange.com/a/53639/71442.

Answer 2

Try below code, here I have taken other data. pca = PCA(n_components = None) first you give here none to check how much each feature is contributing.

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Wine.csv')
X = dataset.iloc[:, 0:13].values
y = dataset.iloc[:, 13].values

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# Applying PCA
from sklearn.decomposition import PCA
pca = PCA(n_components = None)
X_train = pca.fit_transform(X_train)
X_test = pca.transform(X_test)
explained_variance = pca.explained_variance_ratio_
print explained_variance

Output:

array([0.36884109, 0.19318394, 0.10752862, 0.07421996, 0.06245904,
       0.04909   , 0.04117287, 0.02495984, 0.02308855, 0.01864124,
       0.01731766, 0.01252785, 0.00696933])

In the above output you can see the contribution in decreasing order. If you choose n =2 , then 57% of variance you are covering, etc. After reducing dimension, you can choose algorithm for this.