Titanic logistic regression accuracy

Titanic logistic regression accuracy. Other models that also stood out were KNN, SVM, logistic regression, and linear SVC, with all respectable scores. Inherently, it returns the set of probabilities of target class. Jul 25, 2020 · Interpretation: From the result, the odd ratio is 0. In this project, we used Logistic Regression to predict survival outcomes on the Titanic dataset. Jun 22, 2020 · At a closer look, the accuracy scores using cross-validation with Kfold of 10 generated more realistic scores of 84. Singh et al. 40% when I trained and fitted the data. It is a classification algorithm that is used to predict discrete values such as 0 or 1, Malignant or Benign, Spam or Not spam, etc. We'll use a "semi-cleaned" version of the titanic data set, if you use the data set hosted Contribute to rseniic/Accuracy_score-Logistic_Regression- development by creating an account on GitHub. 2' Nov 25, 2021 · Titanic dataset Analysis (~80% accuracy) We experiment with the simple binary classification models like Logistic Regression, SVM, KNN, followed by decision tree based classifiers, like Random Logistic Regression with statsmodels. TN = True negatives. (logistic regression, SVM, random forest, decision trees) were used for the Titanic prediction problem, and logistic regression gave the best accuracy of 83. K — Nearest Neighbor Algorithm. Aug 10, 2021 · DECISION TREE (Titanic dataset) A decision tree is one of most frequently and widely used supervised machine learning algorithms that can perform both regression and classification tasks. import matplotlib. Department of Computer Science & Engineering. Logistic Regression on Titanic Dataset. I want to predict the survival of the passengers using logistic regression. 5 we can take the output as a prediction for the default class (class 0), otherwise the prediction is for the other class (class 1). Nov 6, 2023 · Now we can see that the training accuracy is 85% and the testing accuracy is 84%. Jul 12, 2022 · for LogisticRegression() the accuracy score is 0. 73%. csv file, we have also build Logistic Regression and K-Nearest Neighbour model to predict the survived passengers from the Titanic. In other words, logistic regression is a variation of linear regression where the output variable is a binary or categorical variable. Then each of these sets is further split into subsets to arrive at a decision. 21. Aug 30, 2022 · In this article we will develop a logistic regression model for Titanic survival prediction. 3. The data set contains personal information for 891 passengers, including an indicator variable for their Jun 12, 2020 · Four different algorithms (logistic regression, SVM, random forest, decision trees) were used for the Titanic prediction problem, and logistic regression gave the best accuracy of 83. But, we can also obtain response labels using a probability threshold value. Later we split the TRAIN dataset into x_train,x_test,y_train,y_test and apply Logistic Regression algorithm. We’ll cover data preparation, modeling, and evaluation of the well-known Titanic dataset. In this 1-hour long project-based course, we will predict titanic survivors’ using logistic regression and naïve bayes classifiers. Setting the threshold at 0. 52 % Accuracy), Support Vector Machine (92. Higher accuracy means model is preforming better. : if the Dec 19, 2020 · I then decided to use sklearn’s LogisticRegression() and achieved an accuracy of 79. For Higher accuracy, the model gives best. Sex. csv'). One of the most colorful examples of logistic regression analysis on the internet is survival-on-the-Titanic, which was the subject of a Kaggle data science competition . 1 and 0. On titanic-survival-regression What's inside? a simple, modular, reproducible and portable scikit-learn Logistic Regression (ML) model (pipeline) that predicts whether a passenger would survive given ticket and passenger data. 22%, being 3% less than the made from scratch logistic_regression() function. Data Pre-processing (Cleaning and Preparing Data) Cleaning of data e. However I get stuck while trying to fit the logistic regression model on the training set. Algorithm gives 0. Performed Logistic Regression ( for Classification between two classes that is Survived and Dead) for Titanic Dataset. Now, set one simulation as follow: y. But while trying the multiple solvers when i applied the solver = "multinomial" i got this import sklearn as skl skl. Accuracy and logistic regression In this lab your task is to evaluate a logistic regression model. [6] have observed that the features (P-class, sex, age, children and SibSp) which are selected as more signiﬁcant are highly correlated to the survival of the passengers. EDA and Feature Engineering (In next tutorial) Train/Test split. In the first step I'm doing a very quick data exploration and preprocessing on a visual level, plotting some simple plots to understand the data better. import numpy as np. Load data and fit model# Load modules# Logistic Regression with statsmodels. Updated on Oct 4, 2023. The sinking of the Titanic is one of the key sad tragedies in history and it took place on April 15th, 1912. Oct 4, 2020 · ShareTweet. Multinomial Logistic Regression: Let's say our target variable has K = 4 classes. If you want to read the series from the beginning, here are the links to the A logistic regression model is constructed to predict passenger survival. 5 is a pretty good score for the Titanic dataset. 07% for random forest and 81. Our little journey to machine learning with R continues! Today’s topic is logistic regression – as an introduction to machine learning classification tasks. read_csv('train. pyplot as plt. Data gets separated into explanatory variables ( exog) and a response variable ( endog ). 1 for the two partitions and i'm getting 64. If the age is estimated, is it in the form of xx. We could use repeated random splits, but a View Logistic Regression - Titanic - Jupyter Notebook. TP = True positives. 65 % Accuracy), Decision Tree (92. 53 accuracy - same test is ran on Orange Dec 1, 2004 · Using the data published by Soldner, we have engaged in a logistic regression. We can select the right k value using a small for-loop that Welcome to the Titanic Classification project repository! This project aims to predict whether a passenger on the Titanic survived or not based on various features such as age, gender, class, and more. Here, we are going to use the titanic dataset - source. Let's get their basic idea: 1. We have 9 variables: Pclass: A proxy for socio-economic status (1st = Upper, 2nd = Middle, 3rd = Lower) Name. We want to use the data to create a model that predicts which passengers survived the Titanic shipwreck. Specifying a model is done through classes. 5. Accuracy = TP+TN/TP+FP+FN+TN. The model will be approached as a logistic regression problem, although a Classifier model could also have been used (see the Classification - Iris tutorial). 8156424581005587 for GaussianNB() the accuracy score is 0. analysis of the likelihood of a Titanic passenger surviving the accident, based on his or her. 979 and 0. Maybe better tuning, better features (or predictors) or other algorithms would increase accuracy. Logistic regression can be performed using the glm function with the option family = binomial Nov 22, 2017 · Accuracy is one of the most intuitive performance measure and it is simply a ratio of correctly predicted observation to the total observations. We'll use a "semi-cleaned" version of the titanic data set, if you use the data set hosted Aug 13, 2021 · I covered several ML algorithms and logistic regression with the awesome tidymodels metapackage in R. 8156424581005587 for KNeighborsClassifier() the accuracy score is 0. 5 assumes that we’re not making trade-offs for getting false positives or false negatives, that there normally is a 50 May 4, 2018 · 2172. The two regressions are similar in the sense that they both assume a linear relationship between the predictor and output variables. Sep 7, 2016 · 1. The project showcases the application of logistic regression for binary classification tasks and how to interpret the model's coefficients and make The inverse function of the logit is called the logistic function and is given by: This function takes a value between ]-Inf;+Inf [ and returns a value between 0 and 1; i. I first divide my data frame (total rows = 891) into two data frames i. The code is as follows. 97% on test data set. On May 14, 2021 · Logistic regression comes under the supervised learning technique. import pandas as pd. Sep 13, 2017 · After training a model with logistic regression, it can be used to predict an image label (labels 0–9) given an image. 999. Logistic Regression with the accuracy score of 86. 2/4/2021 Logistic Regression - Titanic - Jupyter Notebook In [2]: import numpy as np import pandas as pd import May 18, 2018 · Titanic? A logistic regression analysis with accuracy and f1-score above 73%, and train and test times around 14 seconds and the most important features to the model involve geographical and Jan 12, 2020 · The logistic regression will then estimate the values for the b parameters that better fit your data, usually using the maximum likelihood method. Centurion University This is a very famous data set and very often is a student's first step in machine learning! We'll be trying to predict a classification- survival or deceased. So we will be using logistic regression model to predict the survival of the passengers in test. Jul 30, 2023 · Logistic Regression; Gradient Boosting; Support Vector Machine (SVM) Model Training and Evaluation We split the preprocessed dataset into training and test sets using the train_test_split function. A deviance of 0 means that the model describes the data perfectly, and a higher value corresponds to a less accurate model. rep[i] ∼ Bernoulli(p[i]) Then, run this simulation, say, 100 times. Then I've done some data cleaning and built a Classifier that can predict whether a passenger survived or not. Logistic regression does not make many of the key assumptions of linear regression and general linear models that are based on ordinary least squares algorithms – particularly regarding linearity, normality, homoscedasticity, Here is my first Kaggle Project - Where I use Logistic Regression to predict the survival of passengers aboard the titanic. In Set Role my attribute name is Sex, in Split Data my ratio is 0. Jun 27, 2021 · We got our best model i. This means that for every increase in 1 year of age, the odds of surviving decreases by 1. 3% for decision tree. The data set contains personal information for 891 passengers, including an indicator variable for their Aug 9, 2021 · titanic_data['GenderClass'] = titanic_data. ROC Nov 6, 2019 · Based on the TitanicCleanData. Therefore I know something is wrong. apply(lambda x: 'child' if x['Age'] < 15 else x['Sex'],axis=1) Logistic regression example 1: survival of passengers on the Titanic. e. We here shape and arrange our TRAIN and TEST datasets. Drag the “Age Value” and “Sex Value” columns into the values section so that they can be Aug 10, 2021 · DECISION TREE (Titanic dataset) A decision tree is one of most frequently and widely used supervised machine learning algorithms that can perform both regression and classification tasks. Explore and run machine learning code with Kaggle Notebooks | Using data from Titanic - Machine Learning from Disaster Jan 15, 2022 · First, let’s download the dataset Titanic Dataset Download. This could be improved upon by using different Machine Learning Models such as a K-NN Classifier or a Random Forrest Classifier Keywords: GGPLOT, confusion matrix, feature engineering, random forest, model evaluation, logistic regression, data mining Introduction The sinking of "The Titanic," which took place on April 15, 1912, is history's most notorious catastrophe. It is a binary classification algorithm used when the response variable is dichotomous (1 or 0). It is a simple and easy to use model and the accuracy of 81. Output: 0. My approach has got 77% accuracy - which for using Logistic Regression is above average. First of all, I will scale my learn and exam data. Jul 10, 2020 · In this article we will be researching on the Titanic Dataset with Logistic Regression and Classification Metrics. 65 %). Logistic regression is chosen for its simplicity and interpretability, making it a suitable starting point for this binary classification problem. For other Accuracy Details Please Check the Project. I am using the glm () function in R. Here our model predicts 162 true positive cases out of 176 positive cases and 94 true negative cases out of Mar 28, 2020 · The simplest classification model is the logistic regression model, and today we will attempt to predict if a person will survive on titanic or not. We will use the Titanic dataset, which is included in the MLDatasets package. 1 Introduction to Logistic Regression Logistic regression is a techinque used for solving the classification problem. e the logistic function takes a linear predictor and returns a probability. The K-Nearest Neighbor algorithm works well for classification if the right k value is chosen. csv Measuring model accuracy with K-fold stratification. Here we provide an example of using shap with logistic regression. We can clearly see that, logistic regression model is more accurate than KNN model on testing data. Problem is after I fit the training datasets and ran predict(), the accuracy returned as 100%, and the scores are returning the same. Implementation of Logistic Regression & SVM for Titanic Survival. __version__ '0. titanic, and that the Fisher scoring (iterative reweighted least squares) algorithm is used to find the maximum likelihood solution of the parameter estimates. train (from row 1 to 800) and test (from row 801 to 891). Before starting, it's worth mentioning there are two ways to do Logistic Regression in statsmodels: statsmodels. My scores at predicting Titanic survivors were ok I guess. Many Titanic components were destroyed in the collision with the iceberg. 96 %), AdaBoostClassifier (89. For project implementation, kindly This is a very famous data set and very often is a student's first step in machine learning! We'll be trying to predict a classification- survival or deceased. pdf from CS AI at Sungkyunkwan. Explore and run machine learning code with Kaggle Notebooks | Using data from Titanic - Machine Learning from Disaster. 8044692737430168. Jan 5, 2017 · Logistic Regression belongs to the family of generalized linear models. Explore and run machine learning code with Kaggle Notebooks | Using data from Titanic - Machine Learning from Disaster We would like to show you a description here but the site won’t allow us. But every time I run it using scikit-learn , it is returning the same results, even when I feed it a different random state. 1% Has any one got a clue how to run logistic regression on Titanic Dataset? I've tried this literally all day but i don't think im getting the right accuracy so i must be missing a step. But doing a single assessment like this may lead to an inaccurate assesment of the accuracy. 1. To solve problems that have multiple classes, we can use extensions of Logistic Regression, which includes Multinomial Logistic Regression and Ordinal Logistic Regression. I will split my learn and exam data to learn_x (for predictors) and learn_ y (for target variable survived) and exam_x (for predictors) exam_y (target variable survived). Logistic regression example 1: survival of passengers on the Titanic. Deviance measures the goodness of fit of a logistic regression model. 8212290502793296 My question is: Content. Oct 29, 2023 · Learning to use logistic regression model using “Titanic - Machine Learning from Disaster” dataset from Kaggle. Keywords: GGPLOT, confusion matrix, feature engineering, random forest, model evaluation, logistic regression, data mining Introduction The sinking of "The Titanic," which took place on April 15, 1912, is history's most notorious catastrophe. g conversion of data, missing value imputation. In our previous example using logistic regression to classify passengers as likely to survive the Titanic, we used a random split for training and test data. Oct 16, 2018 · So, the logistic regression model still seems to be the best model. We would like to show you a description here but the site won’t allow us. 70% of the data is used for training (X_train and y_train), while 30% is used for testing (X_test and y_test). #importing the dataset. python machine-learning prediction classification titanic-survival-prediction titanic-dataset. Here is my code below: #importing the libraries. values. Mar 22, 2022 · I am not an expert on logistic regression, but I thought when solving it using lgfgs it was doing optimization, finding local minima for the objective function. And Classification is nothing but a problem of identifing to which of a set of categories a new observation belongs, on the basis of training dataset containing observations (or instances) whose categorical membership is known. particular characteri Apr 20, 2020 · I'm starting with the regression models in Python, so I used the Titanic dataset from Kaggle. api: The Standard API. It describes the survival status of individual passengers on the Titanic. Contribute to rseniic/Accuracy_score-Logistic_Regression- development by creating an account on GitHub. If the probability is > 0. Some folks on Kaggle got a perfect accuracy, so there is always room for improvement. com Nov 6, 2019 · Based on the TitanicCleanData. Jul 24, 2021 · Measurement of success: Predicting on the test dataset correctly whether the person survived the Titanic or not. In this paper, survival of passengers is figured out using various machine learning techniques namely decision tree, logistic regression and linear SVM. Prediction using Python. Titanic_train = pd. 989, with 95% CI being 0. 83 %), GradientBoostingClassifier (92. Click on the R visual bar plot from the previous section. Jun 29, 2020 · I am using the Logistic Regression for modeling. 79 score on x_test,y_test. 7653631284916201 for GradientBoostingClassifier() the accuracy score is 0. A decision tree split the data into multiple sets. We use Titanic data and predict survival using different variables in data. May 14, 2021 · Logistic regression comes under the supervised learning technique. Logistic Regression using Python Video The first part of this tutorial post goes over a toy dataset (digits dataset) to show quickly illustrate scikit-learn’s 4 step modeling pattern and show the behavior of the logistic Titanic_dataset100-Accuracy 100 % accuracy on a Titanic dataset from kaggle, on which we have to predict whether the person will surive or not Logistic Regression used Explore and run machine learning code with Kaggle Notebooks | Using data from Titanic - Machine Learning from Disaster. Preprocessing May 13, 2019 · Here is how we’re fitting logistic regression. The main focus of this work is to differentiate between the three different machine learning algorithms to analyze the survival rate of traveller based on the accuracy. This technique handles the multi-class problem by fitting K-1 Besides logistic regression, there is a method called knn method which can be used as classification function. Age: Age is fractional if less than 1. Sep 24, 2018 · I tried to use logistic regression for the problem. The Model Information table asserts that we are running a binary logistic regression on the outcome survived in the dataset work. Let's begin our understanding of implementing Logistic Regression in Python for classification. We will also analyze the given Titanic dataset. Oct 11, 2023 · 4. data analysis and feature engineering steps; testing with pytest, and tox to simplify the process Saved searches Use saved searches to filter your results more quickly Performed Logistic Regression ( for Classification between two classes that is Survived and Dead) for Titanic Dataset. Implemented Logistic Regression for Titanic Dataset for Classifying whether or not a person survived the sinking of the Titanic. Logistic Regression on Titanic Dataset We will use the Titanic dataset, which is included in the MLDatasets package. I have the famous titanic data set from Kaggle's website. In this project I'm attempting to do data analysis on the Titanic Dataset. Which model gives the best accuracy–percentage of correct predictions? 1 Compute accuracy in logistic regression Here are the more detailed tasks. Steps involved in a machine learning model: Gathering Data. We demonstrated data preprocessing, exploratory data analysis, model training, and evaluation. About Aug 12, 2019 · The logistic regression model takes real-valued inputs and makes a prediction as to the probability of the input belonging to the default class (class 0). Apr 18, 2019 · Select an age and sex on the 2 slicers. Obtained a accuracy of 80. Explore and run machine learning code with Kaggle Notebooks | Using data from Titanic - Machine Learning from Disaster About this Guided Project. See full list on analyticsvidhya. Logistic regression is the model type which least needs an explainer but it provides a useful example for learning about shap as Shapley values may be compared with model coefficients. 8044692737430168 for SVC() the accuracy score is 0. 09 % Accuracy), Random Forest (90. Saved searches Use saved searches to filter your results more quickly Here's my quick suggestion: Since your dependent variable is binary, you can assume it follows a Bernoulli distribution, with probability given by logistic regression Pri = invlogit(a + bxi). However, alternative machine learning algorithms could be explored for comparison. Once we have those estimators, we can calculate P(Y=1) for new data points and either stick with that probability or use it to classify those observations based on a threshold (ex. 7%. Explore and run machine learning code with Kaggle Notebooks | Using data from Titanic Data set for classification . Python Data Science Project, Titanic Survival Prediction using LogisticRegression (91. Swayanshu Shanti Pragnya. Jul 1, 2021 · 1. Logistic regression is a techinque used for solving the classification problem. When I predicted on the validation dataset I achieved an accuracy of 72. The numbers of survivors were low due to lack of lifeboats for all passengers. qf jv gm xz bq ba dk lg zz fr