{"id":9176,"date":"2022-12-15T20:00:02","date_gmt":"2022-12-15T14:30:02","guid":{"rendered":"http:\/\/myprojectideas.com\/?p=9176"},"modified":"2025-10-22T12:30:32","modified_gmt":"2025-10-22T12:30:32","slug":"employee-salary-prediction-using-machine-learning-machine-learning-project","status":"publish","type":"post","link":"https:\/\/rudelabs.ai\/blogs\/employee-salary-prediction-using-machine-learning-machine-learning-project\/","title":{"rendered":"Employee Salary Prediction Using Machine Learning | Machine Learning Project"},"content":{"rendered":"<h2>Introduction of the Project<\/h2>\n<p>Here we have created a coding project for Employee Salary Prediction using Machine Learning. This machine learning model predicts the salary of the employee based on the number of years of work experience he\/she had in the industry. We have our dataset with two columns which are years of experience and salary, and accordingly, we will train our model using a <a href=\"https:\/\/www.javatpoint.com\/supervised-machine-learning\">Supervised Machine Learning<\/a> algorithm. And finally, we will be able to predict the salary of the employee on the basis of the number of years of work experience.<\/p>\n<iframe loading=\"lazy\"  id=\"_ytid_42953\"  width=\"1080\" height=\"607\"  data-origwidth=\"1080\" data-origheight=\"607\" src=\"https:\/\/www.youtube.com\/embed\/vvM1P0G3W6g?enablejsapi=1&autoplay=0&cc_load_policy=0&cc_lang_pref=&iv_load_policy=1&loop=0&rel=1&fs=1&playsinline=0&autohide=2&theme=dark&color=red&controls=1&\" class=\"__youtube_prefs__  no-lazyload\" title=\"YouTube player\"  allow=\"fullscreen; accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen data-no-lazy=\"1\" data-skipgform_ajax_framebjll=\"\"><\/iframe>\n<p>&nbsp;<\/p>\n<h2>Objectives<\/h2>\n<ul>\n<li>The purpose of building this model is to have a brief idea about the salary of an employee on the basis of how many years an employee has spent in his professional career.<\/li>\n<li>This machine learning model can help businesses to decide what range of salary must be provided to an individual trying to seek a job in the respective organization having relevant years of experience.<\/li>\n<\/ul>\n<h2>Requirements<\/h2>\n<p>As far as requirements while building this Employee Salary Prediction using Machine Learning model are concerned:<\/p>\n<p>1. Python Libraries<\/p>\n<ul>\n<li>Pandas<\/li>\n<li>NumPy<\/li>\n<li>Matplotlib<\/li>\n<\/ul>\n<p>2.\u00a0 <a href=\"https:\/\/jupyter.org\/\">Jupyter Notebook<\/a> or <a href=\"https:\/\/neptune.ai\/blog\/how-to-use-google-colab-for-deep-learning-complete-tutorial\">Google Colab<\/a><\/p>\n<p><em><strong>[Note: All the necessary libraries can be installed by running the pip command directly on the command prompt]<\/strong><\/em><\/p>\n<h2>Source Code<\/h2>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\"># Importing all the necessary libraries\r\n\r\nimport pandas as pd\r\n\r\nimport numpy as np\r\n\r\nfrom matplotlib import pyplot as plt\r\n\r\n%matplotlib inline\r\n\r\n# pandas provide us function to load our data set to our model\r\n\r\n#(using read_csv() function)\r\n\r\ndf = pd.read_csv(\"Salary_Data.csv\")\r\n\r\ndf # Printing the data set (YearsExperience and Salary)\r\n\r\n# Lets have a look over the shape of dataset using shape function!\r\n\r\ndf.shape # Tells us the number of rows and number of colomns in our data set\r\n\r\n# head and tail functions let us know about the first five observations and last five\r\n\r\n#observations in our data set respectively!\r\n\r\ndf.head()\r\n\r\n# prints the first five observation of our data set!\r\n\r\ndf.tail()\r\n\r\n# Prints the last five observations of our data set!\r\n\r\n# This process is to check the wether our dataset is filled with any empty\r\n\r\n# values and tells us through a boolean value to make\r\n\r\n# inference\r\n\r\n# As we can see that entire datset comes out to be with a false value on isna()\r\n\r\n# function which means that no null values are\r\n\r\n# present\r\n\r\ndf.isna()\r\n\r\n# This step is to confirm that no null values are present in our data set\r\n\r\n# so we can move ahead with further operations!\r\n\r\ndf.isna().sum()\r\n\r\n# matplotlib is the library used for plotting the graphs which we have already\r\n\r\n#imported above\r\n\r\n# This means that we need to plot YearsExperience on x-axis and Salary on y-axis\r\n\r\ndf.plot(x = 'YearsExperience' , y = 'Salary' , style = 'o')\r\n\r\n# This title function gives the us the options to provide a specefic title to our\r\n\r\n# plot!\r\n\r\nplt.title('YearsExperience vs Salary')\r\n\r\n# Now its time to give the titles to x-axis and y-axis using xlabel() and ylabel()\r\n\r\n# functions respectively!\r\n\r\n# xlabel() gives the title to the x-axis\r\n\r\nplt.xlabel('Years Of Experience')\r\n\r\n# ylabel() gives the title to y-axis\r\n\r\nplt.ylabel('Salary')\r\n\r\n# Now we have given all the specifications to our graph , so lets have a look on\r\n\r\n# our plot using show() function!\r\n\r\nplt.show()\r\n\r\n# Now its time to make our model which will predict the resuts on the given data\r\n\r\n# So dividing our data into features(inputs) and labels(output) using iloc() function in python\r\n\r\n# This gives us the entire x attributes into an specefic array!\r\n\r\nx = df.iloc[:,:-1].values\r\n\r\nx\r\n\r\n# This gives us the entire y attributes into a specefic array!\r\n\r\ny = df.iloc[:,1].values\r\n\r\n# Lets train our model and test it accordingly!\r\n\r\nfrom sklearn.model_selection import train_test_split\r\n\r\n# We are training our data set with a test_size of 0.2 which means that we are only using 20% of our data to train our model!\r\n\r\nX_train , X_test , y_train , y_test = train_test_split(x , y , test_size = 0.2 , random_state = 0)\r\n\r\n# Importing linear regression algorithm which comes under sklearn\r\n\r\nfrom sklearn.linear_model import LinearRegression\r\n\r\nregressor = LinearRegression()\r\n\r\n# We fitted our training variables in the LinearRegression Model using fit Function\r\n\r\nregressor.fit(X_train , y_train)\r\n\r\n# Linear Regression model called Successfully!\r\n\r\n# Training complete\r\n\r\n# Plotting the regression line\r\n\r\nline = regressor.coef_*x+regressor.intercept_# compare it with equation of straight line [y = mx + c])\r\n\r\n# Plotting for the test data\r\n\r\nplt.scatter(x, y)\r\n\r\nplt.plot(x, line , color = 'red');\r\n\r\nplt.show()\r\n\r\n# Testing our model\r\n\r\nprint(X_test) # these are the attributes of Years Of Experience we have tested!\r\n\r\n# Making predictions on the values we have tested!\r\n\r\ny_pred = regressor.predict(X_test) # Predicted\r\n\r\n# Creating a data frame with Actual and Predicted Values!\r\n\r\ndata_frame = pd.DataFrame({'Actual' : y_test , 'Predicted' : y_pred})\r\n\r\ndata_frame\r\n\r\n# Checking our Model finally at 2.5 years of Experience\r\n\r\nyears_exp = [[10.0]]\r\n\r\nown_pred = regressor.predict(years_exp)\r\n\r\nprint(\"Years of exp is {}\" .format(years_exp))\r\n\r\nprint(\"Predicted salary is {}\" .format(own_pred[0]))<\/pre>\n<h2>Explanation of the Code<\/h2>\n<p>1. Initially, we loaded our dataset in our Jupyer Notebook using the <strong>read_csv function<\/strong> and looked into the first five and last five observations of our dataset using the <strong>head() and tail() functions,<\/strong> respectively.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-18116 size-full\" src=\"https:\/\/rudelabs.ai\/blogs\/wp-content\/uploads\/2022\/12\/Picture2.webp\" alt=\"Employee Salary Prediction using Machine Learning\" width=\"602\" height=\"437\" \/><\/p>\n<p>2. The next step is to check if our dataset contains any <strong>missing values or null values<\/strong>; if they exist, we need to remove the null values from our dataset, and if not, we can move ahead with our dataset with further operations.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-18115 size-full\" src=\"https:\/\/rudelabs.ai\/blogs\/wp-content\/uploads\/2022\/12\/Picture3-1.webp\" alt=\"Employee Salary Prediction using Machine Learning\" width=\"602\" height=\"501\" \/><\/p>\n<p>3. Next, we can use <strong>&#8220;matplotlib&#8221;<\/strong> to create visualizations.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-18114 size-full\" src=\"https:\/\/rudelabs.ai\/blogs\/wp-content\/uploads\/2022\/12\/Picture4-1.webp\" alt=\"Employee Salary Prediction using Machine Learning\" width=\"602\" height=\"454\" \/><\/p>\n<p>4. After this, the linear regression algorithm is loaded by the <strong>&#8220;sklearn&#8221;<\/strong> library, and the model is in the training phase.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-18113 size-full\" src=\"https:\/\/rudelabs.ai\/blogs\/wp-content\/uploads\/2022\/12\/Picture5-1.webp\" alt=\"Employee Salary Prediction using Machine Learning\" width=\"602\" height=\"473\" \/><\/p>\n<p>5. The <strong>best fit line is plotted<\/strong> on our graph, which represents the linear relationship between both variables.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-18111 size-full\" src=\"https:\/\/rudelabs.ai\/blogs\/wp-content\/uploads\/2022\/12\/Picture6-1.webp\" alt=\"Employee Salary Prediction using Machine Learning\" width=\"602\" height=\"537\" \/><\/p>\n<h2>Output<\/h2>\n<p>On running this machine learning source code to predict the salary of an employee successfully, you will get the following output.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-18110 size-full\" src=\"https:\/\/rudelabs.ai\/blogs\/wp-content\/uploads\/2022\/12\/Picture1.webp\" alt=\"Employee Salary Prediction using Machine Learning\" width=\"602\" height=\"253\" \/><\/p>\n<h2>Conclusion<\/h2>\n<p><a id=\"post-9176-_heading=h.gjdgxs\"><\/a> Hence we successfully created a machine learning model to predict the salary of employees based on the number of years of experience in the industry. This Employee Salary Prediction Using Machine Learning model helps businesses to predict the salary of employees and can decide the pay range of the employee working in the company as well as for the employee who is willing to join the company based on the number of years of experience.<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This machine learning model predicts the employee&#8217;s salary based on the number of years of work experience he\/she had in the industry.<\/p>\n","protected":false},"author":1,"featured_media":9183,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[7],"tags":[],"class_list":["post-9176","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-coding-projects"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.1.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Employee Salary Prediction Using Machine Learning | Machine Learning Project - 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