{"id":9160,"date":"2022-12-14T20:00:26","date_gmt":"2022-12-14T14:30:26","guid":{"rendered":"http:\/\/myprojectideas.com\/?p=9160"},"modified":"2025-10-23T12:18:58","modified_gmt":"2025-10-23T12:18:58","slug":"predict-the-percentages-of-students-using-python-machine-learning","status":"publish","type":"post","link":"https:\/\/rudelabs.ai\/blogs\/predict-the-percentages-of-students-using-python-machine-learning\/","title":{"rendered":"Predict The Percentages Of Students Using Python | Machine Learning"},"content":{"rendered":"<h2><a id=\"post-9160-_heading=h.a5lstagbg065\"><\/a>Introduction of the Project<\/h2>\n<p>In this machine learning project, we will predict the Percentages of Students using Python. This is a classic supervised machine learning model that predicts student percentages based on the number of hours they are studying or learning. We will implement a linear regression algorithm to predict the percentage corresponding to Python&#8217;s <a href=\"https:\/\/scikit-learn.org\/stable\/install.html\">&#8220;sklearn&#8221; <\/a>module.<\/p>\n<iframe loading=\"lazy\"  id=\"_ytid_41119\"  width=\"1080\" height=\"607\"  data-origwidth=\"1080\" data-origheight=\"607\" src=\"https:\/\/www.youtube.com\/embed\/TVBBMiBfpFs?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><a id=\"post-9160-_heading=h.r99kt77ya2q2\"><\/a>Objectives<\/h2>\n<ul>\n<li>To predict the percentage of individuals based on specific characteristics, i.e., the number of hours spent studying.<\/li>\n<li>Usage of some graphs and scatter plots in Python&#8217;s Matplotlib library. This is useful for visual analysis of the data.<\/li>\n<li>This model helps students complete their lessons on time and analyze their study plans. And accordingly, they can increase the number of study hours to achieve maximum results.<\/li>\n<\/ul>\n<h2><a id=\"post-9160-_heading=h.vmm85lm59tf3\"><\/a>Requirements<\/h2>\n<p>The requirements for this machine learning model to predict the Percentages of Students using Python:<\/p>\n<ul>\n<li>You need to set up Python on your system. And install all the required libraries, such as pandas, matplotlib, etc. <strong><em>(Note:<\/em><\/strong> <strong><em>These libraries can be installed on Jupyter notebooks using the &#8220;pip command&#8221; from the command prompt or directly.)<\/em><\/strong><\/li>\n<li><a href=\"https:\/\/jupyter.org\/\">Jupyter Notebooks<\/a> or <a href=\"https:\/\/neptune.ai\/blog\/how-to-use-google-colab-for-deep-learning-complete-tutorial\">Google Collab<\/a> to build machine-learning models.<\/li>\n<\/ul>\n<h2><a id=\"post-9160-_heading=h.ynbk7u3zmye5\"><\/a>Source Code<\/h2>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\"># Importing all the necessary libraries needed in our entire analysis\r\n\r\n\r\n\r\n\r\nimport numpy as np\r\n\r\nimport pandas as pd\r\n\r\nimport matplotlib.pyplot as plt\r\n\r\n%matplotlib inline\r\n\r\n# loading the dataset to perform further operations on our given data\r\n\r\ndata_url = \"http:\/\/bit.ly\/w-data\"\r\n\r\ndf = pd.read_csv(data_url)\r\n\r\n# Printing our dataset\r\n\r\ndf\r\n\r\n# checking the shape of data set\r\n\r\ndf.shape # which gives us the result that our dataset contains 25 rows and 2 columns\r\n\r\n# Printing the first five observations using head funtion\r\n\r\ndf.head()\r\n\r\n# Printing the last five observations using tail funtion\r\n\r\ndf.tail()\r\n\r\n# Process to check if any missing value is present in our data set or not!\r\n\r\ndf.isna()\r\n\r\n# This gives us the inference that no missing values are present in our dataset so we can move ahead with further operations!\r\n\r\ndf.isna().sum()\r\n\r\n\r\n\r\n\r\n# Analyzing the scores through plotting the distribution\r\n\r\ndf.plot(x = 'Hours' , y = 'Scores' , style = 'o')\r\n\r\nplt.title('Hours vs Percentage')\r\n\r\nplt.xlabel('Hours Studied')\r\n\r\nplt.ylabel('Percentage Scores')\r\n\r\nplt.show()\r\n\r\n# Checking the statistical results\r\n\r\ndf.describe()\r\n\r\n# Dividing the data into features(input) and labels(output)\r\n\r\nx = df.iloc[:,:-1].values\r\n\r\ny = df.iloc[:,1].values\r\n\r\nprint(x)\r\n\r\nprint(y)\r\n\r\n# Training and test splitting\r\n\r\nfrom sklearn.model_selection import train_test_split\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\nfrom sklearn.linear_model import LinearRegression\r\n\r\nregressor = LinearRegression()\r\n\r\nregressor.fit(X_train , y_train) # training complete\r\n\r\n# Plotting the regression line\r\n\r\nline = regressor.coef_*x+regressor.intercept_\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 Algorithm\r\n\r\nprint(X_test)\r\n\r\ny_pred = regressor.predict(X_test)\r\n\r\n# Creating a data frame of 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 the percentage on the given data point(study hours = 9.25)\r\n\r\nhours = [[7.9]]\r\n\r\nown_pred = regressor.predict(hours)\r\n\r\nprint(\"No of Hours = {}\".format(hours))\r\n\r\nprint(\"Predicted Score = {}\".format(own_pred[0]))\r\n\r\n# Checking the performance of algorithm\r\n\r\nfrom sklearn import metrics\r\n\r\nprint(\"Mean Absolute error is : \" , metrics.mean_absolute_error(y_test , y_pred))<\/pre>\n<h2><a id=\"post-9160-_heading=h.16p8txcvglgy\"><\/a>Explanation of the Code<\/h2>\n<p>1. Building a model begins by first loading the dataset, reviewing the dataset, and concluding that the dataset was successfully loaded into the Jupyter notebook. So initially, we imported all the required libraries and loaded the data accordingly using the <strong>read_csv<\/strong> function of the &#8220;pandas&#8221; library.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-18119 size-full\" src=\"https:\/\/rudelabs.ai\/blogs\/wp-content\/uploads\/2022\/12\/Predict-The-Percentages-Of-Students-Using-Python-2.webp\" alt=\"Predict The Percentages Of Students Using Python | Machine Learning\" width=\"602\" height=\"487\" \/><\/p>\n<p>2. When the dataset is loaded, we check to see if the dataset has <strong>null or null values<\/strong>. If it exists in the dataset, you should remove these values \u200b\u200band convert the dataset to a clean dataset. Once the dataset is clean, you can use it for the following operations:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-18121 size-full\" src=\"https:\/\/rudelabs.ai\/blogs\/wp-content\/uploads\/2022\/12\/Picture3.webp\" alt=\"Predict The Percentages Of Students Using Python | Machine Learning\" width=\"602\" height=\"96\" \/><\/p>\n<p>3. Next, use <strong>matplotlib to create the visualization<\/strong>.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-18122 size-full\" src=\"https:\/\/rudelabs.ai\/blogs\/wp-content\/uploads\/2022\/12\/Picture4.webp\" alt=\"Predict The Percentages Of Students Using Python | Machine Learning\" width=\"524\" height=\"468\" \/><\/p>\n<p>4. Then, the linear <strong>regression algorithm is loaded by the sklearn library<\/strong>, and the model is in the training phase.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-18123 size-full\" src=\"https:\/\/rudelabs.ai\/blogs\/wp-content\/uploads\/2022\/12\/Picture5.webp\" alt=\"Predict The Percentages Of Students Using Python | Machine Learning\" width=\"553\" height=\"195\" \/><\/p>\n<p>5. The best fit line of Linear Regression is fitted into our plot, and now it\u2019s time to create a new data frame and predict the percentage of students.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-18124 size-full\" src=\"https:\/\/rudelabs.ai\/blogs\/wp-content\/uploads\/2022\/12\/Picture6.webp\" alt=\"Predict The Percentages Of Students Using Python | Machine Learning\" width=\"602\" height=\"266\" \/><\/p>\n<p>6. To check the accuracy of our model, we use the concept of mean absolute error, which gives the accuracy and lets us know how efficient our model is and how close the predicted values are to the actual values.<\/p>\n<h2><b>Output<\/b><\/h2>\n<p>On running this machine learning source code successfully, you will get the following output.<\/p>\n<p><a id=\"post-9160-_heading=h.gjdgxs\"><\/a> <img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-18125 size-full\" src=\"https:\/\/rudelabs.ai\/blogs\/wp-content\/uploads\/2022\/12\/Predict-The-Percentages-Of-Students-Using-Python.webp\" alt=\"Predict The Percentages Of Students Using Python | Machine Learning\" width=\"602\" height=\"240\" \/><\/p>\n<h2><a id=\"post-9160-_heading=h.mg7iwfasy49p\"><\/a>Conclusion<\/h2>\n<p>Hence we developed a machine learning model to predict the Percentages of Students using Python, which was successfully able to predict the marks of a student on the basis of a given feature, that was, the number of hours of learning. We observed that the model predicted the values close to the actual values resulting in the conclusion that our model is efficient enough to predict the percentage of a student on the basis of the number of hours devoted to his work of study.<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this machine learning project, we will predict the Percentages of Students using Python based on the number of hours devoted to studying.<\/p>\n","protected":false},"author":1,"featured_media":9172,"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-9160","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>Predict The Percentages Of Students Using Python | Machine Learning - 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