![]() If R² is 0.8 it means 80% of the variation in the output can be explained by the input variable. It is the measure of goodness of fit of the model. ![]() R-Squared(R²) is also known as the coefficient of determination, It is the proportion of variation in Y(dependent or target variable) explained by the independent variables X. We are using Two Performance metric for this problem statement. ![]() This Dataset Has the following attributes:ġ.full_name : Asteroid object with full name and designation.Ģ.a : Semi-major axis of an Asteroid in AU(Astronomical Units).Ĥ.i : Inclination of an Asteroid in degrees.ĥ.om : Longitude of the ascending node of an Asteroid in degrees.Ħ.w : Argument of the perihelion in degrees.ħ.q : perihelion distance of an Asteroid in AU(Astronomical Units).Ĩ.ad : Aphelion distance of an Asteroid in AU(Astronomical Units).ġ0.data_arc : number of days spanned by the data_arc.ġ1.condition_code : MPC(Minor Planet Center) ‘U’(Uncertainity) parameter for any Asteroid.ġ2.G : Magitude slope parameter of an Asteroid.ġ3.n_obs_used : No of all types of Radar Observations used.ġ4.H : Absolute Magnitude Parameter in mag.ġ6.extent : Asteroid’s bi/tri axial ellipsoid dimensions in km.ġ8.rot_per : Rotational Period of an Asteroid measured in h.ġ9.GM : Standard gravitational parameter, Product of mass and gravitational constant.Ģ0.BV : Color index B-V magnitude difference in mag.Ģ1.UB : Color index U-B magnitude difference in mag.Ģ2.IR : Color index I-R magnitude difference in mag.Ģ3.spec_B : Spectral taxonomic type(SMASSII).Ģ4.spec_T : Spectral taxonomic type(Tholen).Ģ6.pha : Potentially Hazardous Asteroid(flag Y/N).Ģ7.moid : Earth Minimum orbit Intersection Distance in AU. The Source of the dataset can be found hereįor this problem statement, we are using Asteroid.csv for predicting the Asteroid Diameter. Impact conditions such as asteroid size and speed, but also density and impact angle determine the kinetic energy released in an impact event causes the asteroid to hit the earth. The most probable impact angle is 45 degrees. We are doing this because we need to prevent any damage caused by the Asteroid.ĭue to Earth’s escape velocity, the minimum impact velocity is 11 km/s with asteroid impacts averaging around 17 km/s on the Earth. This problem which we would be dealing here is a regression problem which will help us in predicting an estimate diameter for an Asteroid. ![]() There are millions of Asteroids but most of them mainly live in the Asteroid belt where it lies between Mars and Jupiter. Python Data Science Machine Learning Asteroid Diameter Prediction Case Study 1Īsteroids are small rocky objects that revolve around the sun like planets.Įven though they orbit around the sun just like our planets do they are much smaller than our planets. ˹Earlier˺ we tried to reach heaven ˹for news˺, only to find it filled with stern guards and shooting stars. ![]() Python Data Science Machine Learning Using Machine Learning To Predict Asteroid Diameter Most Probable Impact Angle Is 45 Degrees Minimum Impact Velocity Is 11 km/s With Asteroid Impacts Averaging 17 km/s Asteroid Dataset Exploratory Data Analysis(PDF) Exploratory Data Analysis PDF Exploratory Data Analysis-EDA Data Exploration PDF Challenge Asteroid Diameter Prediction Through Machine Learning Using Linear Regression ML Model Predicting the Size, Shape And Possible Impacts of An Asteroid Detection and Risk Prediction of Asteroid Impact On Planet Earth Machine Learning Model Asteroid Diameter Prediction Case Study 1 Data Science Data Analysis Univariate Analysis Bivariate Analysis Multivariate Analysis Data Analytics Basic Exploratory Data Aalysis Exploratory Data Analysis EDA(PDF) Exploratory Data Analysis PDF EDA Exploratory Data Analysis-EDA Data Exploration PDF Plotting PDFs and CDFs Code Snippets And Outputs To Perform EDA, Build ML Models to Predict the Diameter Feature Correlation Matrix Split Independent and Dependent Features Perform Train-Test-Validation Train Regression Model and Calculate the Performance Metrics Linear Regression R Squared Negative Mean Absolute Error Ridge Regression R Squared Negative Mean Absolute Error Lasso Regression R Squared Negative Mean Absolute Error ElasticNet Regression R Squared Negative Mean Absolute Error Train the Models Using Ensembles AdaBoost Regression R Squared Negative Mean Absolute Error Random Forest Regression R Squared Negative Mean Absolute Error XGBoost Regression R Squared Negative Mean Absolute Error Checking the Performance of all Models R Squared Negative Mean Absolute Error Learn to Code In Python, Machine Learning, Matplotlib, Numpy, Pandas ![]()
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