# [Tutor] Curve Fitting a cvs data file in Python

Alan Gauld alan.gauld at yahoo.co.uk
Sat Nov 9 12:44:13 EST 2019

```On 09/11/2019 04:19, Eddwin Shoemo via Tutor wrote:
> Hi. I was presented with a question and it has me stumped.

In what way? You have et us a lot of code but no idea what you
want to know.

We will not do your homework for you. We will try to help steer
you in the right direction. But few of us are likely to try running
arbitrary code sent through the internet. So you need to tell us
what happens, what you expected to happen plus any error
messages you get(in full)

> My PY and csv files are attached.

The code is inline but the csv data seems to have gotten lost.
The server doesn't trust many attachments.

> Here is the question:
>
> Use the function, test_func1(x, b0, b1,b2,b3,b4,b5)  (<??????located at the bottom of the script below)
> to predict the SALES in terms of each of the 3 attributes: the TV, Radio, and Newspaper data. PLOT the results.
> HINT: use the optimize library from scipy module. look for functions such as curve_fit.
>
> My Script:
>
> import csv
> import pandas as pd
> import numpy as np
> from scipy import optimize
> from scipy.optimize import curve_fit
> import matplotlib.pyplot as plt  #Used to plot
> from sklearn.linear_model import LinearRegression, Ridge, Lasso
> from sklearn.model_selection import cross_val_score, GridSearchCV # for optimuim MSE with Linear reg
>
> ########## Another data set -advertising
> print(data2.head()) # print the first 5 lines
> #print(data2)
> data2.drop(['Unnamed: 0'], axis=1, inplace=True)# drop the unnamed column (axis=1 for col, and 0 for rows)
>
> #tv=data2['TV'].values  # Alternative command for the one listed below
> tv=data2[['TV']]
>
> #print(tv)
> newspaper=data2['newspaper'].values
> #print(newspaper)
> #sales=data2['sales'].values
> sales=data2[['sales']]
>
> """

This effectively comments out most of the rest of the code.
Is that what you intended?

> reg1 = LinearRegression()
> reg1.fit(tv, sales)
> y_pred1 = reg1.predict(tv)
> plt.figure()
> plt.scatter(tv, sales, color='y') #plot real values
> plt.plot(tv, y_pred1, "r.") #plot predicted values # The dot displays a dotted value
> ##   COLORS: one of {'b', 'g', 'r', 'c', 'm', 'y', 'k', 'w'}
>
> reg2 = LinearRegression()
> reg2.fit(radio.reshape(-1,1), sales)  # Will use a column vector
> plt.scatter(radio, sales, color='b') #plot real values
> plt.xlabel("The number of Ads (TV, Radio, and Newspaper)") # Label for the graph
> plt.ylabel("The Sales") # Label for the graph
>
> reg3 = LinearRegression()
> reg3.fit(newspaper.reshape(-1,1), sales)
> y_pred3 = reg3.predict(newspaper.reshape(-1,1))
> plt.scatter(newspaper, sales, color='red') #plot real values
> plt.plot(newspaper, y_pred3, "g.")
>
> ### Ridge Regression ###############################################
> ridge = Ridge()
> parameters_dict = {'alpha':[1e-12, 1e-7, 1e-3, 1e-2, 1, 4, 15, 20]}
> rg_regressor = GridSearchCV(ridge,parameters_dict,scoring='neg_mean_squared_error',cv=5)
> rg_regressor.fit(tv,sales)
> y_pred4 = rg_regressor.predict(tv)
> print('The best value for -Ridge- alpha is', rg_regressor.best_params_)
> print('The minimuim MSE -Ridge-is',rg_regressor.best_score_)
> plt.scatter(tv,sales,color='m') # Plot real values
> plt.plot(tv, y_pred4, "k--")
>
> ### Lasso Regression ###############################################
> lasso = Lasso()
> parameters_dict_lasso = {'alpha':[1e-12, 1e-7, 1e-3, 1e-2, 1, 4, 15, 20]}
> ls_regressor = GridSearchCV(lasso, parameters_dict_lasso,scoring='neg_mean_squared_error', cv=5)
> ls_regressor.fit(tv,sales)
> y_pred5 = ls_regressor.predict(tv)
> print('The best value for Lasso alpha is', ls_regressor.best_params_)
> print('The minimuim MSE -Lasso-is', ls_regressor.best_score_)
> plt.scatter(tv,sales,color='m') # Plot real values
> plt.plot(tv, y_pred5, "g--") # -- is how the plot is displayed
>
> ### Linear Regression with optimuim MSE #############################
> LR = LinearRegression()
> MSEs=cross_val_score(LR,tv,sales,scoring='neg_mean_squared_error', cv=5)
> avg_MSEs=np.mean(MSEs)
> print('The average MSE-Linear', avg_MSEs)
> LR.fit(tv, sales)
> Pred = LR.predict(tv)
> plt.scatter(tv,sales,color='m') # Plot real values
> plt.plot(tv, Pred, "b--") # -- is how the plot is displayed
> """
>
>
> def test_func1(x, b0, b1,b2,b3,b4,b5):
>   return (b0 + b1*x + b2*x**2+ b3*x**3 + b4*x**4+ b5*x**5)
>
> print('call the function:',test_func1(1,10,5,4,3,2,1))

This is all just plain math, no need for data or anything else.
I have no idea how it relates to the question or the code above.

--
Alan G
Author of the Learn to Program web site
http://www.alan-g.me.uk/
http://www.amazon.com/author/alan_gauld
Follow my photo-blog on Flickr at:
http://www.flickr.com/photos/alangauldphotos

```