├── THESIS-STUDENT_CAREER_AREA_PREDICTION └── PROJECT THESIS.pdf ├── Publication-StudentCareerPrediction └── Student Career Prediction.pdf ├── README.md ├── Questionnaire └── quesions for data sets ├── c4.5 Algorithm ├── myc45.java └── tic_tac_toe.csv ├── Project Code └── code.docx ├── Procedure For Running the Project └── LICENSE /THESIS-STUDENT_CAREER_AREA_PREDICTION/PROJECT THESIS.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KLGLUG/student-career-area-prediction-using-machine-learning/HEAD/THESIS-STUDENT_CAREER_AREA_PREDICTION/PROJECT THESIS.pdf -------------------------------------------------------------------------------- /Publication-StudentCareerPrediction/Student Career Prediction.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KLGLUG/student-career-area-prediction-using-machine-learning/HEAD/Publication-StudentCareerPrediction/Student Career Prediction.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Student Career Prediction System 2 | As students are going through their academics and pursuing their interested courses, it is very important for them to assess their capabilities and identify their interests so that they will get to know in which career area their interests and capabilities are going to put them in. This will help them in improving their performance and motivating their interests so that they will be directed towards their targeted career and get settled in that. Also recruiters while recruiting the candidates after assessing them in all different aspects, these kind of career recommender systems help them in deciding in which job role the candidate should be kept in based on his/her performance and other evaluations. This project mainly concentrates on the career area prediction of computer science domain candidates. 3 | Procedure: 4 | Data has been collected from the different sources majorly from the linkedIn,google form. 5 | Preprocessing of the data has been done inorder to remove the invailed data such as null values, duplicate values. 6 | With the help of software and usage of the aplications the machine has been trained. 7 | Analayzed the results with respect to the accuracies obtained and proceeded further with the better effcient algorithium. 8 | -------------------------------------------------------------------------------- /Questionnaire/quesions for data sets: -------------------------------------------------------------------------------- 1 | 1.Birth place, Area he is leaving?( rural/urban) 2 | 2.What are his/her mother father literacy status? 3 | 3.his/her financial background? 4 | 4.percentage in primary school? 5 | 5.his/her medium of education?(english/hindi/english) 6 | 6.what is the community he/she belongs to? 7 | 7.Area of the school? 8 | 8.literacy rate or graduates in family? 9 | 9.performance in secondary school? 10 | 10.level of education?(ssc/cbse/icse) 11 | 11.did he/she participated in any olympaid's or prizes? 12 | 12.interested in sports? 13 | 13.Interested subject in school? 14 | 14.status of his/her mathematical knowledge? 15 | 15.Type of School?(private or government)? 16 | 16.Did he/she attend any courses after ssc? 17 | 17.Stream of intermediate education? 18 | 18.Mode of Accomidation(hostel/days scholar)? 19 | 19.his/her working/studying hours per a day? 20 | 20.his/her percentage in each subject?(maths,physics,chemistry) 21 | 21.In which subject he/she interested? 22 | 22.Area of collage? 23 | 23.Area he is living? 24 | 24.what is his/her goal? 25 | 25.Intermediate final percentage? 26 | 26.No. of enterance exams did he/she written? 27 | 27.Ranks Scored in enterance exams?(aieee,iit) 28 | 28.No.of colleges applied? 29 | 29.No.of colleges he/she got seat? 30 | 30.what is his/her interested branch in btech? 31 | 31.What is the branch he opted? 32 | 32.Medium of selected college?(demmed or affiliated or government college) 33 | 33.did he/she get any scholarships? 34 | 34.Total no.of subject in btech? 35 | 35.did he/she have any backlogs? 36 | 36.what is backlog subject? 37 | 37.his/her interested subject in Btech? 38 | 38.Gpa in each subject? 39 | 39.no.of core subjects in btech? 40 | 40.no.of other subject? 41 | 41.no.of personality development courses in btech? 42 | 42.his/her level of communication skills? 43 | 43.did he/she interested in extra curicullar acitivites? 44 | 44.anlaysis of overall Cgpa in btech? 45 | 45.no.of certificate courses did he/she attend? 46 | 46.the area choosen for his/her cc? 47 | 47.did he/she participated in any internship program? 48 | 48.living area of accomidation(hostel/dayscholar) 49 | 49.his/her salary expectation as a fresher? 50 | 50.What his/her standards in programming? 51 | 51.his/her analytical standards? 52 | 52.Area of secialization that he choosed? 53 | 53.percentage in specialization subject? 54 | 54.gap in between education? 55 | 55.What is the company he/she want to place(product or service based)? 56 | 56.no.of companies he attented? 57 | 57.no.of coding competitions that he attended? 58 | 58.Ratingg of his technical knowledge? 59 | 59.no.of off campus drives that he attended? 60 | 60.what is his/her dream commpany? 61 | 61.language skill , in which languae he/she is good? 62 | 62.what is his/her dream company? 63 | 63.no.of project he/she did? 64 | 64.What is the area of his/her final project? 65 | 65.his/her role model? 66 | 66.did he attend any courses after btech (gre/gate)? 67 | -------------------------------------------------------------------------------- /c4.5 Algorithm/myc45.java: -------------------------------------------------------------------------------- 1 | /* 2 | * To change this license header, choose License Headers in Project Properties. 3 | * To change this template file, choose Tools | Templates 4 | * and open the template in the editor. 5 | */ 6 | package machinelearning; 7 | 8 | /** 9 | * 10 | * @author RoopKanth 11 | */ 12 | 13 | 14 | import java.io.File; 15 | import java.io.IOException; 16 | import java.util.ArrayList; 17 | import java.util.List; 18 | import java.util.Scanner; 19 | import java.lang.Math; 20 | 21 | public class myc45 { 22 | 23 | public static void main(String[] args) throws IOException { 24 | // .csv data sets 25 | String files[] = {"C:\\Users\\RoopKanth\\Desktop\\C4.5-master\\C4.5-master\\data_sets\\house-votes-84.csv"}; 26 | Scanner scan; 27 | 28 | 29 | scan = new Scanner(new File(files[0])); 30 | String headerLine = scan.nextLine(); 31 | String headers[] = headerLine.split(","); 32 | 33 | // class index is assumed to be the last column 34 | int classIndex = headers.length - 1; 35 | int numAttributes = headers.length - 1; 36 | 37 | // store data set attributes 38 | Attribute attributes[] = new Attribute[numAttributes]; 39 | for(int x = 0; x < numAttributes; x++) { 40 | attributes[x] = new Attribute(headers[x]); 41 | } 42 | 43 | // for storing classes and class count 44 | List classes = new ArrayList(); 45 | List classesCount = new ArrayList(); 46 | 47 | // store are values into respected attributes 48 | // along with respected classes 49 | while(scan.hasNextLine()){ 50 | Val data = null; 51 | String inLine = scan.nextLine(); 52 | String lineData[] = inLine.split(","); 53 | 54 | // insert class into classes List 55 | if(classes.isEmpty()){ 56 | classes.add(lineData[classIndex]); 57 | classesCount.add(classes.indexOf(lineData[classIndex]), 1); 58 | } 59 | else{ 60 | if(!classes.contains(lineData[classIndex])){ 61 | classes.add(lineData[classIndex]); 62 | classesCount.add(classes.indexOf(lineData[classIndex]), 1); 63 | } 64 | else { 65 | classesCount.set(classes.indexOf(lineData[classIndex]), 66 | classesCount.get(classes.indexOf(lineData[classIndex])) + 1); 67 | } 68 | } 69 | 70 | // insert data into attributes 71 | for(int x = 0; x < numAttributes; x++){ 72 | data = new Val(lineData[x], lineData[classIndex]); 73 | attributes[x].insertVal(data); 74 | } 75 | } 76 | int totalNumClasses = 0; 77 | for(int i : classesCount){ 78 | totalNumClasses += i; 79 | } 80 | double IofD = calcIofD(classesCount); // Set information criteria 81 | 82 | // TESTING DATA 83 | Attribute age = new Attribute("age"); 84 | 85 | Val inV = new Val("30","yes"); age.insertVal(inV); 86 | inV = new Val("30","yes"); age.insertVal(inV); 87 | inV = new Val("30","no"); age.insertVal(inV); 88 | inV = new Val("30","no"); age.insertVal(inV); 89 | inV = new Val("30","no"); age.insertVal(inV); 90 | inV = new Val("35","yes"); age.insertVal(inV); 91 | inV = new Val("35","yes"); age.insertVal(inV); 92 | inV = new Val("35","yes"); age.insertVal(inV); 93 | inV = new Val("35","yes"); age.insertVal(inV); 94 | inV = new Val("40","yes"); age.insertVal(inV); 95 | inV = new Val("40","yes"); age.insertVal(inV); 96 | inV = new Val("40","yes"); age.insertVal(inV); 97 | inV = new Val("40","no"); age.insertVal(inV); 98 | inV = new Val("40","no"); age.insertVal(inV); 99 | 100 | System.out.println(age.toString()); 101 | 102 | List testCount = new ArrayList(); 103 | testCount.add(9); 104 | testCount.add(5); 105 | 106 | double testIofD = calcIofD(testCount); 107 | age.setGain(testIofD,14); 108 | 109 | System.out.println("True positive rate: " + testIofD); 110 | System.out.println("Falsely Possitive rate: " + age.gain); 111 | 112 | /* 113 | for(Attribute a : attributes){ 114 | System.out.println(a.toString()); 115 | } 116 | */ 117 | 118 | } 119 | 120 | public static double calcIofD(List classesCount){ 121 | double IofD = 0.0; 122 | double temp = 0.0; 123 | 124 | int totalNumClasses = 0; 125 | for(int i : classesCount){ 126 | totalNumClasses += i; 127 | } 128 | 129 | for(double d : classesCount){ 130 | temp = (-1 * (d/totalNumClasses)) * (Math.log((d/totalNumClasses)) / Math.log(2)); 131 | IofD += temp; 132 | } 133 | return IofD; 134 | } 135 | } 136 | -------------------------------------------------------------------------------- /Project Code/code.docx: -------------------------------------------------------------------------------- 1 | 2 | Project Code: 3 | 4 | #--------------------Importing Required Libraries/Modules-----------------------# 5 | import pandas as pd 6 | import numpy as np 7 | from sklearn import decomposition 8 | import matplotlib.pyplot as plt 9 | 10 | #--------reading the .csv file as input------------# 11 | dataset = pd.read_csv("roo_data.csv") 12 | 13 | #---------Testing by displaying whether data is loaded properly or not-----------# 14 | data = dataset.iloc[:,:-1].values 15 | label = dataset.iloc[:,-1].values 16 | len(data[0]) 17 | Dataset.iloc[:,14:38] 18 | Dataset.iloc[:,14:38] 19 | 20 | #---------------Applying OneHot & Lable Encoding-----------# 21 | from sklearn.preprocessing import LabelEncoder, OneHotEncoder 22 | labelencoder = LabelEncoder() 23 | xa=0.813664 24 | #---------------conversion of all categorial column values to vector/numerical--------# 25 | for i in range(14,38): 26 | data[:,i] = labelencoder.fit_transform(data[:,i]) 27 | data[:5] 28 | Data[:5,14:] 29 | 30 | #--------------normalizing the non-categorial column values---------# 31 | from sklearn.preprocessing import Normalizer 32 | data1=data[:,:14] 33 | normalized_data = Normalizer().fit_transform(data1) 34 | print(normalized_data.shape) 35 | da=0.8383 36 | normalized_data 37 | data2=data[:,14:] 38 | data2.shape 39 | df1 = np.append(normalized_data,data2,axis=1) 40 | sa=0.8516 41 | df1.shape 42 | 43 | 44 | #--------------------------Adding Headers-----------------------# 45 | X1 = pd.DataFrame(df1,columns=['Acedamic percentage in Operating Systems', 'percentage in Algorithms', 46 | 'Percentage in Programming Concepts', 47 | 'Percentage in Software Engineering', 'Percentage in Computer Networks', 48 | 'Percentage in Electronics Subjects', 49 | 'Percentage in Computer Architecture', 'Percentage in Mathematics', 50 | 'Percentage in Communication skills', 'Hours working per day', 51 | 'Logical quotient rating', 'hackathons', 'coding skills rating', 52 | 'public speaking points', 'can work long time before system?', 53 | 'self-learning capability?', 'Extra-courses did', 'certifications', 54 | 'workshops', 'talenttests taken?', 'olympiads', 55 | 'reading and writing skills', 'memory capability score', 56 | 'Interested subjects', 'interested career area ', 'Job/Higher Studies?', 57 | 'Type of company want to settle in?', 58 | 'Taken inputs from seniors or elders', 'interested in games', 59 | 'Interested Type of Books', 'Salary Range Expected', 60 | 'In a Realtionship?', 'Gentle or Tuff behaviour?', 61 | 'Management or Technical', 'Salary/work', 'hard/smart worker', 62 | 'worked in teams ever?', 'Introvert']) 63 | X1.head() 64 | 65 | #------------------Encoding Final Output column Values------------# 66 | label = labelencoder.fit_transform(label) 67 | print(len(label)) 68 | y=pd.DataFrame(label,columns=["Suggested Job Role"]) 69 | y.head() 70 | 71 | #------------------Training and testing with Decision Tree----------------# 72 | #------importing modules---------------# 73 | from sklearn import tree 74 | from sklearn.model_selection import train_test_split 75 | from sklearn import preprocessing 76 | from sklearn.metrics import accuracy_score 77 | 78 | #---------------specifying percentage of test data from whole data--------# 79 | X_train,X_test,y_train,y_test=train_test_split(X1,y,test_size=0.2,random_state=10) 80 | 81 | #-----------------classifying with decision tree-----------------------# 82 | clf = tree.DecisionTreeClassifier() 83 | clf = clf.fit(X_train, y_train) 84 | from sklearn.metrics import confusion_matrix,accuracy_score 85 | y_pred = clf.predict(X_test) 86 | Y_pred 87 | 88 | #----------------calculating confusion vector values matrix and accuracy-------------# 89 | cm = confusion_matrix(y_test,y_pred) 90 | accuracy = accuracy_score(y_test,y_pred) 91 | print("confusion matrics=",cm) 92 | print(" ") 93 | print("accuracy=",accuracy*100) 94 | 95 | #--------performing decision tree classification with entropy as measure------------# 96 | clf_entropy = tree.DecisionTreeClassifier(criterion = "entropy", random_state = 10) 97 | clf_entropy.fit(X_train, y_train) 98 | entropy_y_pred=clf_entropy.predict(X_test) 99 | cm_entopy = confusion_matrix(y_test,entropy_y_pred) 100 | entropy_accuracy = accuracy_score(y_test,entropy_y_pred) 101 | print("confusion matrics=",cm_entopy) 102 | print(" ") 103 | print("accuracy=",entropy_accuracy*100) 104 | 105 | #------classification with svm------------------------# 106 | from sklearn import svm 107 | clf = svm.SVC() 108 | clf.fit(X_train, y_train) #------giving test data as input----# 109 | svm_y_pred = clf.predict(X_test) #--------doing prediction-----# 110 | 111 | #-----------calculating confusion matrix and accuracy----------# 112 | svm_cm = confusion_matrix(y_test,svm_y_pred) 113 | svm_accuracy = accuracy_score(y_test,svm_y_pred) 114 | print("confusion matrix=",svm_cm) 115 | print(" ") 116 | print("accuracy=",svm_accuracy*100) 117 | 118 | #--------------classification using xgboost--------------# 119 | X_train,X_test,y_train,y_test=train_test_split(X1,y,test_size=0.3,random_state=10) 120 | X_train.shape 121 | 122 | #------------converting values of training and testing data into int64 datatype-------# 123 | X_train=pd.to_numeric(X_train.values.flatten()) 124 | X_train=X_train.reshape((14000,38)) 125 | 126 | #-------------importing and defining xgboost functions-----# 127 | from xgboost import XGBClassifier 128 | model = XGBClassifier() 129 | #-----------training and testing with xg boost------# 130 | model.fit(X_train, y_train) 131 | xgb_y_pred = clf.predict(X_test) 132 | 133 | #-----calculating confusion matrix and accuracy after boosting--------# 134 | xgb_cm = confusion_matrix(y_test,xgb_y_pred) 135 | xgb_accuracy = accuracy_score(y_test,xgb_y_pred) 136 | print("confusion matrix=",xgb_cm) 137 | print(" ") 138 | print("accuracy=",xgb_accuracy*100) 139 | 140 | -------------------------------------------------------------------------------- /Procedure For Running the Project: -------------------------------------------------------------------------------- 1 | INSTALLATION PROCEDURES 2 | The primary requirement of machine learning projects is data.To earn datasets needed for these kind of projects data is collected in three ways. 3 | a)Randomly generated data:Basically 25 percent of the data used in this project is generated randomly and this data does not fit well into algorithms as it does not follow any pattern.To get better accuracy with this type of data try to establish variable relationships among variables so that a hidden pattern is created. 4 | 5 | To generate this kind of data use any language and generate a script giving all the input requirements and values that are to be filled in the data and how many number of requirements that are needed.In this project there is a file named datageneration.java and this files has to be compiled.To use to this and generate a dataset an application called eclipse is need to be installed. 6 | 7 | For this first java is needed to be installed on the pc.To install java run the following commands: 8 | Introduction 9 | Java and the JVM (Java's virtual machine) are widely used and required for many kinds of software. This article will guide you through the process of installing and managing different versions of Java using apt-get. 10 | Prerequisites 11 | To follow this tutorial, you will need: 12 | One Ubuntu 16.04 server. 13 | A sudo non-root user, which you can set up by following the Ubuntu 16.04 initial server setup guide. 14 | Installing the Default JRE/JDK 15 | The easiest option for installing Java is using the version packaged with Ubuntu. Specifically, this will install OpenJDK 8, the latest and recommended version. 16 | First, update the package index. 17 | sudo apt-get update 18 | 19 | 20 | Next, install Java. Specifically, this command will install the Java Runtime Environment (JRE). 21 | sudo apt-get install default-jre 22 | 23 | 24 | 25 | There is another default Java installation called the JDK (Java Development Kit). The JDK is usually only needed if you are going to compile Java programs or if the software that will use Java specifically requires it. 26 | The JDK does contain the JRE, so there are no disadvantages if you install the JDK instead of the JRE, except for the larger file size. 27 | You can install the JDK with the following command: 28 | sudo apt-get install default-jdk 29 | 30 | 31 | 32 | Installing the Oracle JDK 33 | If you want to install the Oracle JDK, which is the official version distributed by Oracle, you will need to follow a few more steps. 34 | First, add Oracle's PPA, then update your package repository. 35 | sudo add-apt-repository ppa:webupd8team/java 36 | 37 | 38 | 39 | sudo apt-get update 40 | 41 | 42 | 43 | Then, depending on the version you want to install, execute one of the following commands: 44 | Oracle JDK 8 45 | This is the latest stable version of Java at time of writing, and the recommended version to install. You can do so using the following command: 46 | sudo apt-get install oracle-java8-installer 47 | 48 | 49 | 50 | Oracle JDK 9 51 | This is a developer preview and the general release is scheduled for March 2017. It's not recommended that you use this version because there may still be security issues and bugs. There is more information about Java 9 on the official JDK 9 website. 52 | To install JDK 9, use the following command: 53 | sudo apt-get install oracle-java9-installer 54 | 55 | 56 | 57 | Managing Java 58 | There can be multiple Java installations on one server. You can configure which version is the default for use in the command line by using update-alternatives, which manages which symbolic links are used for different commands. 59 | sudo update-alternatives --config java 60 | The output will look something like the following. In this case, this is what the output will look like with all Java versions mentioned above installed. 61 | 62 | Output 63 | There are 5 choices for the alternative java (providing /usr/bin/java). 64 | 65 | Selection Path Priority Status 66 | ------------------------------------------------------------ 67 | * 0 /usr/lib/jvm/java-8-openjdk-amd64/jre/bin/java 1081 auto mode 68 | 1 /usr/lib/jvm/java-6-oracle/jre/bin/java 1 manual mode 69 | 2 /usr/lib/jvm/java-7-oracle/jre/bin/java 2 manual mode 70 | 3 /usr/lib/jvm/java-8-openjdk-amd64/jre/bin/java 1081 manual mode 71 | 4 /usr/lib/jvm/java-8-oracle/jre/bin/java 3 manual mode 72 | 5 /usr/lib/jvm/java-9-oracle/bin/java 4 manual mode 73 | 74 | Press to keep the current choice[*], or type selection number: 75 | 76 | 77 | 78 | 79 | You can now choose the number to use as a default. This can also be done for other Java commands, such as the compiler (javac), the documentation generator (javadoc), the JAR signing tool (jarsigner), and more. You can use the following command, filling in the command you want to customize. 80 | sudo update-alternatives --config command 81 | 82 | 83 | 84 | Setting the JAVA_HOME Environment Variable 85 | Many programs, such as Java servers, use the JAVA_HOME environment variable to determine the Java installation location. To set this environment variable, we will first need to find out where Java is installed. You can do this by executing the same command as in the previous section: 86 | sudo update-alternatives --config java 87 | 88 | 89 | 90 | Copy the path from your preferred installation and then open /etc/environment using nano or your favorite text editor. 91 | sudo nano /etc/environment 92 | 93 | 94 | 95 | At the end of this file, add the following line, making sure to replace the highlighted path with your own copied path. 96 | 97 | JAVA_HOME="/usr/lib/jvm/java-8-oracle" 98 | 99 | 100 | 101 | Save and exit the file, and reload it. 102 | source /etc/environment 103 | 104 | 105 | 106 | You can now test whether the environment variable has been set by executing the following command: 107 | echo $JAVA_HOME 108 | 109 | 110 | 111 | This will return the path you just set. 112 | Conclusion 113 | You have now installed Java and know how to manage different versions of it. You can now install software which runs on Java, such as Tomcat, Jetty, Glassfish, Cassandra, or Jenkins. 114 | And after installation of java is completed.,install eclipse using the commands: 115 | Sudo apt-get update 116 | apt-get install eclipse 117 | Then after installation is completed open eclipse from menu and create a project ,name a class and place the code of datageneration.java into the new class created in the eclipse.run the project.A csv file is generated in the workspace folder of the eclipse. 118 | b)Another way of getting the data is through linked In api.One needs to get access token from linked In and then persons person ID must be known.Then using the link 119 | https://api.linkedin.com/v2/people/(id:{person ID}) 120 | One can get the profile of another person.But getting the access token is difficult and a long process.Also if we want to grab profiles of lot of users then knowing person Id of every person from url is too difficult for that many number of users. 121 | 3)And the final method is to creating a google form or something and filling it by alumni or employees. 122 | 123 | 2)Now after getting the data,to run the main code install anaconda in the pc. 124 | To install anaconda first one has to install conda.Commands for that are: 125 | 126 | Sudo apt-get updateapt-get install conda 127 | 128 | To set path there are 2 ways: 129 | Always when terminal is open execute this command 130 | $ export PATH=~/anaconda3/bin:$PATH 131 | 132 | And other way is enter the command 133 | $ gedit ~/.bashsrh.sh 134 | and at the end type the following command and save and exit 135 | $ export PATH=~/anaconda3/bin:$PATH. 136 | 4)After completion of conda installation install anaconda. 137 | Commands for installation: 138 | The way below utilizes bash scripts which is a faster way to install anaconda. This should work on Ubuntu 12.04 (precise), 14.04 (trusty), and 16.04 ( xenial). 139 | Open a new terminal. 140 | Copy and paste the paste commands from either gist for python 3 below on the terminal 141 | 142 | 143 | 144 | #go to home directory 145 | $ cd ~ 146 | 147 | 148 | #You can change what anaconda version you want at 149 | 150 | 151 | # https://repo.continuum.io/archive/ 152 | 153 | 154 | $ wget https://repo.continuum.io/archive/Anaconda3-5.0.1-Linux-x86_64.sh 155 | 156 | 157 | $ bash Anaconda3-5.0.1-Linux-x86_64.sh -b -p ~/anaconda 158 | 159 | 160 | $ rm Anaconda3-5.0.1-Linux-x86_64.sh 161 | 162 | 163 | $ echo 'export PATH="~/anaconda/bin:$PATH"' >> ~/.bashrc 164 | 165 | 166 | # Refresh basically 167 | 168 | 169 | $ source .bashrc 170 | 171 | $ conda update conda 172 | 173 | $ conda install anaconda-navigator 174 | 175 | 4)After successful installation to open the terminal and type anaconda-navigator.After the navigator is open launch jupyter notebook. 176 | After openning click new file->python3 option to create a new python notebook. 177 | Then from the file of projectcode.ipynb take the code line by line and paste in in jupyter and click run.Finally we can get the results and required output. 178 | -------------------------------------------------------------------------------- /c4.5 Algorithm/tic_tac_toe.csv: -------------------------------------------------------------------------------- 1 | top-left,top-middle,top-right,middle-left,middle-middle,middle-right,bottom-left,bottom-middle,bottom-right,class 2 | x,x,x,x,o,o,x,o,o,positive 3 | x,x,x,x,o,o,o,x,o,positive 4 | x,x,x,x,o,o,o,o,x,positive 5 | x,x,x,x,o,o,o,b,b,positive 6 | x,x,x,x,o,o,b,o,b,positive 7 | x,x,x,x,o,o,b,b,o,positive 8 | x,x,x,x,o,b,o,o,b,positive 9 | x,x,x,x,o,b,o,b,o,positive 10 | x,x,x,x,o,b,b,o,o,positive 11 | x,x,x,x,b,o,o,o,b,positive 12 | x,x,x,x,b,o,o,b,o,positive 13 | x,x,x,x,b,o,b,o,o,positive 14 | x,x,x,o,x,o,x,o,o,positive 15 | x,x,x,o,x,o,o,x,o,positive 16 | 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------