Are you a newbie when it comes to Data Analysis and Data modeling? If yes, then you are in the right place. In this blog, we are going to be performing some Exploratory Data Analysis on the HR Dataset available in . We’ll also be using RandomForest to predict who left their company. This is a beginner-friendly dataset and it is easy to work with. With that out of the box, let’s get into the juicy stuff. Kaggle IMPORTING The first thing we always do when we start with our work is to import the libraries. You don’t need to import every single library that you’ll be using in the notebook right at the beginning itself. You can start with the bare minimum and those include: Just the above are enough when you start working on a problem. From then on you can just add them at the start or you can just import them where ever you are in the notebook. import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline Now that we are done with importing let’s load the data. df = pd.read_csv("../input/hr-analytics/HR_comma_sep.csv") df.head() CHECKING FOR THE NEATNESS OF THE DATA Now that the data is loaded. Let’s take a look at how the data is, i.e, the data types, the no of NaN values etc., We can do that by using the .info() method df.info() As you can see above, all the cols are filled with no NaN values. float, int and object are the data types of the columns. for col in df.columns: print(f" { col } - ", df[col].unique()) print() There seem to be no weird values in the data, which is very good. Let’s look at the metrics. df.describe() Now that we took a look at the data, Let’s get into my favorite part, EDA. EXPLORATORY DATA ANALYSIS (EDA) In this, we will be visualizing the data. By doing this we will be able to further understand how the data is and if there is any work that is to be done. sns.set()#sets the style of the plot. fig = plt.figure(figsize=(12,6))#Used to display the plot sns.barplot(x='Department', y='satisfaction_level', hue='salary', data=df, ci=None) plt.title("Satisfaction_level Vs Department", size=15) plt.show() The ones with higher salaries are more satisfied. But product_mng said hold my cup. It seems like the ones with low salaries are more satisfied than the ones with high salaries. The same is for the land. fig = plt.figure(figsize=(12,6)) g = sns.barplot(x='Department', y='last_evaluation', data=df, ci=None) g.bar_label(g.containers[0]) plt.title("Last Evaluation Vs Department", size=15) plt.show() Everybody has a similar level of evaluation done. fig = plt.figure(figsize=(12,6)) sns.barplot(x='Department', y='number_project', data=df, ci=None) plt.title("Number Project Vs Department", size=15) plt.show() fig = plt.figure(figsize=(12,6)) g = sns.barplot(x='Department', y='Work_accident', data=df, ci=None) g.bar_label(g.containers[0]) plt.title("Work Accident Vs Department", size=15) plt.show() fig = plt.figure(figsize=(12,6)) g = sns.barplot(x='Department', y='satisfaction_level', hue='left', data=df, ci=None) g.bar_label(g.containers[0]) g.bar_label(g.containers[1], rotation=90) plt.title("Left Vs Department with satisfaction level", size=15) plt.show() fig = plt.figure(figsize=(12,6)) g = sns.barplot(x='Department', y='satisfaction_level', hue='left', data=df, ci=None) g.bar_label(g.containers[0]) g.bar_label(g.containers[1], rotation=90) plt.title("Left Vs Department with satisfaction level", size=15) plt.show() fig = plt.figure(figsize=(12,6)) sns.lineplot(x='Department', y='average_montly_hours', data=df, ci=None, color='g', marker='o') plt.title("Avg Hours spent in the company per Department", size=15) plt.show() The chart looks hilarious lol🤣 Don’t know why🤷♂️ fig = plt.figure(figsize=(12,6)) sns.lineplot(x='Department', y='promotion_last_5years', data=df, ci=None, color='black', marker='o') plt.title("Promotions of Last 5 years in the company per Department", size=15) plt.show() The management and the marketing department have more promotions when compared to the others. fig = plt.figure(figsize=(12,6)) salary = df['salary'].value_counts() sns.lineplot(x=salary.values, y=salary.index, ci=None, color='orange', marker='o') plt.title("Salary (Counts) in the company", size=15) plt.show() Now that we’ve seen how the data is let’s get into modeling. Before getting into that let’s take a look at the correlation. sns.heatmap(df.corr(), center=0, linewidth=1, annot=True, fmt='.2f') plt.show() Here if we observe we can see that satisfaction_level and left columns are highly negatively correlated, which I think is quite obvious. MODELING We need to split our data into a training set and test set so that the model doesn’t remember the data by heart. Before doing that let us drop the categorical columns and ‘Development’ ‘salary’. df = df.drop(['Department', 'salary'], axis=1) Here we are passing in a list of col names that we want to drop. By specifying we are telling it to remove the column. axis=1 You can do it like this as well df.drop(['Department', 'salary'], axis=1, inplace=True) By doing this you don’t need to specifically assign it df, since it is going to do it in place. Now that we got rid of them let's split our data. First, separate the target var(the one we want to predict) and the rest of the data. X = df.drop('left', axis=1) # we will predict who left y = df['left'] Then we pass the X, y into the following function. From the name of the function, we can say that it is going to be splitting our data into training and test sets. from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3) Now we just need to fit the training data to the model of our choice. Since we are trying to predict we can go with RandomForest RandomForest can do both prediction and regression. we are also going to be using GridSearchCV to find the best params for our model. from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import GridSearchCV param = [ {'n_estimators': [100, 200, 300, 400, 450, 500], 'max_depth': [3, 4, 6, 8, 10, 12], 'max_leaf_nodes': [15, 20, 25]}, ] rf = RandomForestClassifier() gs_rf = GridSearchCV(rf, param, cv = 5, n_jobs = -1, verbose = 1) gs_rf.fit(X_train, y_train) rf_best = gs_rf.best_estimator_ pred = gs_rf.predict(X_test) gs_rf.best_estimator_ So finally we are finished with the modeling. The RandomForest gave us 98% accuracy. Wow really nice. Well done if you followed until the end. CONCLUSION In this blog we have seen: A basic workflow of things How to implement RandomForest How to implement GridSearchCV You can check out my Kaggle notebook and give it an upvote if you found it helpful. here I hope that you found this analysis helpful and interesting. If you liked my work then don’t forget to follow me on and , for more content on productivity, self-improvement, Coding, and Tech. Also, check out my works on and follow me on . Medium YouTube Kaggle LinkedIn Also Published Here
