diff --git a/README.md b/README.md
index d3544a3..9c5d47f 100644
--- a/README.md
+++ b/README.md
@@ -13,23 +13,23 @@
 
 ### Portfolio Projects
 - GIS & Spatial Analysis:
-    - Title: ["Examining Geospatial Covariate Relationships with Civilian Killings in South Sudan’s Civil War"](./GIS/GIS_covariate_relationships-killings-south_sudan.html)
+    - **Title:** ["Examining Geospatial Covariate Relationships with Civilian Killings in South Sudan’s Civil War"](./GIS/GIS_covariate_relationships-killings-south_sudan.html)
         - Focus: Geospatial Statistics
         Technology Used: QGIS, R, GeoDa
         - Contents: Geospatial interpolation, hot spot analysis, Moran's I calculation, Moran's I residual analysis, Lagrange Multiplier (LM) and Robust LM lag and error diagnostics, and Spatial Durbin models.
 - Data Visualization:
-    - Title: **"UNAMID: Did the UN’s Withdrawal from Darfur Lead to More Violence against Civilians?"**
+    - **Title: "UNAMID: Did the UN’s Withdrawal from Darfur Lead to More Violence against Civilians?"**
     - [Version not showing code](./Data%20Viz-Darfur%20Violence%20as%20UN%20Left/visual_1-darfur_violence.html)
     - [Version showing all code](./Data%20Viz-Darfur%20Violence%20as%20UN%20Left/visual_1-darfur_violence-code_included.html)
         - Focus: GIS Mapping
         - Technology Used: R Markdown, ggmap, tmap, sp, sf, rgdal, stadia/stamen maps, plotly, dplyr, ggplot2, ggthemes, ggpubr, stringr, scales, and kableExtra
         - Contents: Interactive and static charts, GIS maps, heat map tables, density maps, and union buffers and using statistics within them
 - Natural Language Processing (NLP):
-    - Title: ["Using News Articles on Events in Nigeria in 2019 to Predict Political Violence"](./ML-Predicting%20Violence%20with%20News/Using_Nigerian_News-based_ML_Models_to_Predict_Political_Violence.html)
+    - **Title:** ["Using News Articles on Events in Nigeria in 2019 to Predict Political Violence"](./ML-Predicting%20Violence%20with%20News/Using_Nigerian_News-based_ML_Models_to_Predict_Political_Violence.html)
         - Focus: NLP for Time-Series Forecasting
         - Technology Used: Python, TF-IDF word transformation, NLTK library, Scikit-Learn machine learning models, Scikit-Learn's TimeSeriesSplit, Augmented Dickey-Fuller Test, first-differencing, lags
         - Contents: Converting news articles by publishing date into time-series machine elarning forecasting models. Performance comparison between Ridge, Lasso, Random Forest, and XGBoost regression models
-    - Title: **"LDA Topic Modeling & VADER Sentiment Analysis for Political News Articles on Events Related to Nigeria in 2019"**
+    - **Title: "LDA Topic Modeling & VADER Sentiment Analysis for Political News Articles on Events Related to Nigeria in 2019"**
     - [Primary document (Python)](./NLP-Topic%20Models%20&%20Sentiment/Nigeria_News_LDA_&_Sentiment_Analysis.html)
     - [Visualization for the project(R)](./NLP-Topic%20Models%20&%20Sentiment/Nigeria_News_Sentiment_Analysis-Viz-Created-in-R.html)
         - Focus: Topic Modeling & Sentiment Analysis