1. Find a simple problem to solve using an ML model
2. Use Google Colab to import data samples, and create EDAs and Model
3. Create our Exploratory Data Analysis
4. Build a simple linear regression model to predict the outcome
5. Save/Serialize the model to use it in production
6. Create Project Structure (boilerplate) to work with Flask
7. Create the API endpoints with Flask
8. Deploy the model to a real server using DigitalOcean
9. Resources

Step #1 - Find a simple problem to solve using an ML model

Step #2- Use Google Colab to create data, EDA and Model

import pandas as pd

csv_in_drive = "/content/drive/MyDrive/your_path/salary.csv"

df = pd.read_csv(csv_in_drive)
df.head()

Step #3- Create our Exploratory Data Analysis

import seaborn as snsimport matplotlib.pyplot as plt
from sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LinearRegressionfrom sklearn.metrics import r2_score
import pandas as pd

csv_in_drive = "/content/drive/MyDrive/6- Marketing/1-Blog Posts/1- Salary Prediction Blog Post/salary.csv"

df = pd.read_csv(csv_in_drive)

df.head()

"""# Importing Libraries"""

import seaborn as sns
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score

"""# EDAs

This is a simple Exploratory Data Analysis
"""

# to check the amount of data
df.shape

# To check if we have any null values in the dataset
df.isnull().values.any()

## dividing data
train_set, test_set = train_test_split(df, test_size=0.2, random_state=42)

## train_set copy
df_copy = train_set.copy()

## exploratory analysis
df_copy.describe()

# search for correlations
df_copy.corr()

df_copy.plot.scatter(x='YearsExperience', y='Salary')

"""We have intentionally created a high correlated dataset to build a simple linear regression"""

sns.regplot('YearsExperience', 'Salary', data=df_copy)

Step #4- Build a simple linear regression model to predict the outcome

"""# Building the Model
Now that we have a high correlted data, we want to build the model
"""

## building the model
test_set_full = test_set.copy()
test_set = test_set.drop(["Salary"], axis=1)

test_set.head()

train_labels = train_set["Salary"]
train_labels.head()

## with train data
train_set_full = train_set.copy()
train_set = train_set.drop(["Salary"], axis=1)

train_set.head()

import warnings
warnings.filterwarnings(action="ignore", module="scipy", message="^internal gelsd")

lin_reg = LinearRegression()
lin_reg.fit(train_set, train_labels)

print("Coefficients", lin_reg.coef_)
print("Intercept", lin_reg.intercept_)

salary_pred = lin_reg.predict(test_set)
salary_pred

print(salary_pred)
print(test_set_full['Salary'])

"""Seems like we have good results comparing 'real' data with the 'predicted' data"""

# Lets check the scores

lin_reg.score(test_set, test_set_full["Salary"])

r2_score(test_set_full["Salary"], salary_pred)

r2_score = (test_set_full["Salary"], salary_pred)
r2_score

plt.scatter(test_set_full["YearsExperience"], test_set_full["Salary"], color="blue")
plt.plot(test_set_full["YearsExperience"], salary_pred, color="red", linewidth=2)

Step #5- Save/Serialize the model to use it in production

"""# Persisting the Model"""

## Model persistence
import pickle

with open ("/content/drive/MyDrive/6- Marketing/1-Blog Posts/1- Salary Prediction Blog Post/python_lin_reg_model.pkl", "wb") as file_handler:
    pickle.dump(lin_reg, file_handler)
    
with open ("/content/drive/MyDrive/6- Marketing/1-Blog Posts/1- Salary Prediction Blog Post/python_lin_reg_model.pkl", "rb") as file_handler:
    loaded_pickle = pickle.load(file_handler)
    
loaded_pickle

# Just for testing purposes, lets use the joblib instead of pickle:

import joblib

joblib.dump(lin_reg, "/content/drive/MyDrive/6- Marketing/1-Blog Posts/1- Salary Prediction Blog Post/linear_regression_model.pkl")

"""# Conclusion

With this we end a very simple model, this will help us to understand how to deploy de result to production
"""

Step #6- Create Project Structure (boilerplate) to work with Flask

• ORIGINAL COLAB FILE: https://colab.research.google.com/drive/1wEaJNVUKb6zigl0AWYuoLjy_CkyaIiMW
• BLOG POST COLAB FILE:
• https://colab.research.google.com/drive/13o5u6Hwvs1GlnTL7EVRyLovQZQiqUhN3?authuser=4
• SLIDE FILE: https://docs.google.com/presentation/d/1WCwaAq4d4zowfgtCU1dVaQYy8AwnwuXk/edit#slide=id.p7