Reviews
There are no reviews yet. Be the first to send feedback to the community and the maintainers!
Repository Details
More Repositories
EDA-on-zomato-dataset.
check-whether-a-Orthopedic-patients-is-Normal-or-Abnormalnot
KNN :K-nearest neighbors (KNN) algorithm is a type of supervised ML algorithm which can be used for both classification as well as regression predictive problems. However, it is mainly used for classification predictive problems in industry. The following two properties would define KNN well โ Lazy learning algorithm โ KNN is a lazy learning algorithm because it does not have a specialized training phase and uses all the data for training while classification. Non-parametric learning algorithm โ KNN is also a non-parametric learning algorithm because it doesnโt assume anything about the underlying dataData-Cleaning-Mthod---Deleting-rows-and-columns
polynomial-linear-regression-implements-
DAY-1-Python-Fundamental-basics-introduction
Introduction-to-Handson-Data-Manipulation-with-PYTHON
Linear-Regression
Linkedin-Profile-Data-Analysis-using-Power-BI
breat-cancer-prediction-and-model-accuracy-measurement
Model-Selection-and-Cross-Validation
10.Linear-Regression-Covariance-variance-calculation-
Learn-Pandas-in-python
NLP
Breast-Cancer-Prediction-AND-Model-Accuracy-Measurement
HierarChical-Clustering
how to use Dendrogram to Find Optimal number of ClustersEDA-on-Wind-Data-Set
SALARY-Prediction
Linear Regression : Linear regression is useful for finding relationship between two continuous variables. One is predictor or independent variable and other is response or dependent variable.All-you-need-to-know-about-SEABORN-
TextHero-Text-Preprocessing-in-NLP-
-Analyses-the-features-of-Orthopedic-patients-and-making-a-decision-whether-a-patients-is-Normal-or-
#Analyses the features of Orthopedic patients and making a decision whether a patients is Normal or Abnormal. Algorithms Used : K-NN Testing Score : 83% K-NN Training Score : 75% Naive Bayes Testing Score : 76% Naive Bayes Training Score : 78% Precision = TP / (TP + FP) Precision : 0.886 Recall = TP /(TP + FN) Recall : 0.8703 F1_score = 2*(Precision*Recall)/(Precision+Recall) F1_score : 0.8785 Key Activities : 1. Normalization 2. EDA 3. Classification_report 4. StandardScaler 5. Feature importanceNeuro_Science
Hierarchical-
GRIP-internship-task
Internship submissionHTML_Basic_01
3D_UNET
Statistics-Books-for-Data-Science
DEEP-LEARNING-PREREQUISITES-
Patronizing-and-Condescending-Language-PCL-Detection
DSA-WITH-PYTHON
DSA-with-C-
573-pankaj
This is the Descriptions of my Data Journey !!Bank-Customers-Segmentation-Power-BI-Dashboard
Linear-Algebra-for-Data-Science
Decision-Tree-Algorithm
All you need to Know about decision treeMODEL-Accuracy-Measure-ment
Learn-Numpy-in-python
it's includes the essential techniques of numerical python towards the DATA SCIENCEEDA_on_Breast_Cancer_Dataset
Minor-Project-2nd-semester
Data-Science-Interview-Preparation
Work-on-Pima-Indians-Diabetes-Database
This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset.MLproject
Model-Selection-using-Cross-Validation
How you select which model is best for your data set based on model accuracy ?iNeuron-Assignments-FSDA-Batch-2022-June-to-DEC
Breast-Cancer-Data-Analysis