India Tourism Analytics Dashboard: Key Insights from 13,000 Tourist Reviews & Visits

By abhishek.verma75000 · July 1, 2026

Explored 13,000 tourism records to analyze visitor satisfaction, travel trends, sentiment, destination rankings, and recommendation patterns across India.…

The analysis identified the top 10 tourist places based on a combined score of average user rating and final recommendation score. Two bar charts and supporting data tables were generated to visualize the results clearly.

The analysis produced two charts showing how visit volumes vary across seasons and months for each category. A grouped bar chart displays visit counts per season broken down by category, and a line chart tracks monthly visit trends for each category throughout the year. Both visualizations are available for you to explore the patterns interactively.

No, paid places do NOT score higher than free entry places. In fact, free entry places have a very slightly higher average final recommendation score (0.6689) compared to paid places (0.6686) — but the difference is essentially negligible at just 0.0003 points. Both groups score nearly identically around 0.67.

The analysis reveals which Indian states and cities attract the most visits and earn the highest recommendation scores. Two bar charts were generated showing the top 10 states and top 10 cities ranked by total visits, with color coding to indicate average recommendation scores. Tamil Nadu is the most-visited state with 1,917 visits and an average recommendation score of 0.66, while Rajasthan leads all states with the highest average recommendation score of 0.675.

The analysis reveals that crowd level has virtually no meaningful correlation with sentiment score, user rating, or likelihood to recommend. Two charts and data tables were generated to visualize these findings. The grouped bar chart shows average metrics across Low, Medium, and High crowd levels, while the correlation heatmap confirms the near-zero relationships between crowd level and satisfaction measures.

Weather conditions and suitability scores have surprisingly little impact on user ratings and revisit intent. Two charts and supporting tables were generated to visualize the findings. Across all weather conditions, ratings are nearly identical — 'Clear' weather leads slightly with an average rating of 4.387 and a 68.1% revisit rate, while 'Windy' conditions score 4.368 with a 69.2% revisit rate. The weather suitability score shows virtually no correlation with user ratings (correlation: -0.005), meaning higher suitability scores don't meaningfully predict better ratings. Interestingly, the 0.2–0.4 suitability range actually produced the highest average ratings (4.393), while the lowest range (0.0–0.2) still achieved a strong 70.0% revisit rate.

The analysis breaks down tourist place categories by travel type, showing visit counts, percentage shares, and average recommendation scores. Two visualizations were generated: a grouped bar chart showing visit counts across categories and travel types, and a heatmap displaying average recommendation scores per category-travel type combination.

The analysis successfully produced a dual-axis line chart showing visit volume and average ratings by year, along with data tables summarizing yearly and monthly trends. Visit counts peaked in 2022 with 2,205 visits, and average user ratings showed a slight decline from 4.387 in 2019 to 4.373 in 2024 — a very marginal drop overall. The yearly chart lets you visually compare how visit volumes and ratings moved together across the years.

A bubble chart and supporting data tables have been generated comparing the top 10 cities by average final recommendation score. In the bubble chart, each city is plotted by its rank (x-axis) and average recommendation score (y-axis), with bubble size representing average entry fee in INR and bubble color indicating average visit duration in hours. This makes it easy to spot which highly-ranked cities also have lower fees (smaller bubbles) — those represent the best value for money.

The analysis produced 4 visualizations (3 seasonal box plots + 1 correlation heatmap) and detailed seasonal statistics. Box plots show how temperature, humidity, and rainfall vary across seasons, while the heatmap reveals how weather relates to visitor behavior. Summer is the hottest and wettest season by average values, but Monsoon scores highest on overall weather extremity when factoring in variability and rainfall spread. Weather variables show virtually no correlation with visitor behavior — temperature vs. user rating is -0.011 and rainfall vs. crowd level is -0.002 — suggesting that ratings and crowd sizes are driven by destination attributes rather than weather conditions.

A word cloud has been generated from the 'review text' column of your dataset. The visualization displays the most frequently occurring words in the reviews, with larger words appearing more prominently to indicate higher frequency.

The sentiment analysis of your review text column reveals that the overwhelming majority of reviews are positive. Out of 13,000 reviews analyzed, nearly 76% express positive sentiment, about 20% are neutral, and only 4.2% are negative. A bar chart has been generated showing the sentiment distribution across all three categories.