From 9c08304c94edc0f81690d3060bb82e511ac194a2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Dar=C3=ADo=20Here=C3=B1=C3=BA?= Date: Tue, 23 Apr 2019 11:29:59 -0300 Subject: [PATCH] Syntax issue on string 14 --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index e8d954a..99466ac 100644 --- a/README.md +++ b/README.md @@ -11,20 +11,20 @@ The repository is a learning exercise to: * Evaluate and interpret my results and justify my interpretation based on observed data set * Create notebooks that serve as computational records and document my thought process. -The analysis is divided into four sections, saved in juypter notebooks in this repository -1. Identifying the problem and Data Sources +The analysis is divided into four sections, saved in jupyter notebooks in this repository +1. Identifying the problem and Data Sources 2. Exploratory Data Analysis 3. Pre-Processing the Data -4. Build model to predict whether breast cell tissue is malignant or Benign +4. Build model to predict whether breast cell tissue is malignant or benign ### [Notebook 1](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB1_IdentifyProblem%2BDataClean.ipynb): Identifying the problem and Getting data. -**Notebook goal:Identify the types of information contained in our data set** +**Notebook goal: Identify the types of information contained in our data set** In this notebook I used Python modules to import external data sets for the purpose of getting to know/familiarize myself with the data to get a good grasp of the data and think about how to handle the data in different ways.  ### [Notebook 2](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB2_ExploratoryDataAnalysis.ipynb) Exploratory Data Analysis -**Notebook goal:  Explore the variables to assess how they relate to the response variable** +**Notebook goal: Explore the variables to assess how they relate to the response variable** In this notebook, I am getting familiar with the data using data exploration and visualization techniques using python libraries (Pandas, matplotlib, seaborn. Familiarity with the data is important which will provide useful knowledge for data pre-processing) ### [Notebook 3](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB3_DataPreprocesing.ipynb) Pre-Processing the data -**Notebook goal:Find the most predictive features of the data and filter it so it will enhance the predictive power of the analytics model.** +**Notebook goal: Find the most predictive features of the data and filter it so it will enhance the predictive power of the analytics model.** In this notebook I use feature selection to reduce high-dimension data, feature extraction and transformation for dimensionality reduction. This is essential in preparing the data before predictive models are developed. ### [Notebook 4](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB4_PredictiveModelUsingSVM.ipynb) Predictive model using Support Vector Machine (svm) **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.**