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

df = pd.read_csv("peak_4.csv", index_col = 0)
print(df.columns)
print(df.dtypes)

    from sklearn.linear_model import Lasso
X = df.drop('uRNA ', axis = 1).values
y = df['uRNA '].values
names = df.drop('uRNA ', axis = 1).columns
lasso = Lasso(alpha = 1)
lasso_coef = lasso.fit(X, y).coef_
plt.bar(names, lasso_coef)
plt.xticks(rotation = 45)
plt.show()
    from sklearn.linear_model import Lasso
from sklearn.model_selection import train_test_split

#prepare data
X = df.drop('uRNA ', axis = 1).values
y = df['uRNA '].values

#split data
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size= 0.3, random_state= 42)
names = df.drop('uRNA ', axis = 1).columns

scores = []

for alpha in [0.01, 1.0, 10.0, 20.0, 50.0]:
    lasso = Lasso(alpha = alpha)
    lasso.fit(X_train, y_train)
    lasso_pred = lasso.predict(X_test)
    scores.append(lasso.score(X_test, y_test))

print(scores)
    #trying cross validation

from sklearn.model_selection import cross_val_score, KFold
from sklearn.linear_model import LinearRegression
kf = KFold(n_splits = 6, shuffle = True, random_state = 51)
reg = LinearRegression()
cv_results = cross_val_score(reg, X, y, cv = kf)
print(cv_results)

    example of a standardization

    from numpy import asarray from sklearn.preprocessing import StandardScaler

    define data

    data = asarray([[100, 0.001], [8, 0.05], [50, 0.005], [88, 0.07], [4, 0.1]]) print(data)

    define standard scaler

    scaler = StandardScaler()

    transform data

    scaled = scaler.fit_transform(data) print(scaled)

    from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaled = scaler.fit_transform(X)

lasso = Lasso(alpha = 1)
lasso_coef = lasso.fit(scaled, y).coef_
plt.bar(names, lasso_coef)
plt.xticks(rotation = 45)
plt.show()

    Final code for factorial optimization

    import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import Lasso
from sklearn.preprocessing import StandardScaler

#preparing data
df = pd.read_csv("peak_4.csv", index_col = 0)
df['NTP_to_mg'] = (df['NTP']*4)/df['Magnesium']
df['T7_to_template'] = df['T7']/df['Template']
X = df.drop('uRNA ', axis = 1).values   #features
y = df['uRNA '].values                  #dependable
names = df.drop('uRNA ', axis = 1).columns  #names of features

scaler = StandardScaler()  #creating model for scaling
scaled = scaler.fit_transform(X)  # creating transformed set of features

# fitting the model
lasso = Lasso(alpha = 0.1)
lasso_coef = lasso.fit(scaled, y).coef_  # extracting coefficients of features impact

# plotting the features impact
plt.bar(names, lasso_coef)
plt.xticks(rotation = 45)
plt.show()

    import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import Lasso
from sklearn.preprocessing import StandardScaler

#preparing data
df = pd.read_csv("peak_5_data.csv", index_col = 0)
df['NTP_to_mg'] = (df['NTP']*4)/df['Magnesium']
df['T7_to_template'] = df['T7']/df['Template']
X = df.drop('uRNA', axis = 1).values   #features
y = df['uRNA'].values                  #dependable
names = df.drop('uRNA', axis = 1).columns  #names of features

scaler = StandardScaler()  #creating model for scaling
scaled = scaler.fit_transform(X)  # creating transformed set of features

# fitting the model
lasso = Lasso(alpha = 0.1)
lasso_coef = lasso.fit(scaled, y).coef_  # extracting coefficients of features impact

# plotting the features impact
plt.bar(names, lasso_coef)
plt.xticks(rotation = 45)
plt.show()