diff --git a/510_23BAI10157_23BAI10274/README.md b/510_23BAI10157_23BAI10274/README.md
new file mode 100644
index 0000000..ef752fc
--- /dev/null
+++ b/510_23BAI10157_23BAI10274/README.md
@@ -0,0 +1,72 @@
+# 🧠 Synapse — AI-Powered Study Planner
+
+Synapse is an intelligent, personalized study planner that uses **Reinforcement Learning (Q-Learning)** to generate optimized study roadmaps based on your strengths and weaknesses.
+
+It combines AI decision-making with a clean GUI to help you focus on what matters most.
+
+---
+
+## 🚀 Features
+
+- 🎯 Personalized Study Plan based on your current skill levels  
+- 🧠 AI-Powered Scheduling using Q-learning  
+- 📊 Adaptive Learning – improves recommendations over time  
+- 🔄 Balanced Subject Distribution (avoids repetition & burnout)  
+- 💡 Actionable Tasks for each subject (Beginner → Advanced)  
+- 🖥️ Modern GUI built with Tkinter  
+
+---
+
+## 🗂️ Project Structure
+
+.
+├── app.py
+├── brain.py
+├── logic.py
+└── q_table.json
+
+---
+
+## ⚙️ How It Works
+
+1. Input your confidence levels for each subject (0–100%)
+2. The AI identifies weak areas
+3. Generates a study sequence using reinforcement learning
+4. Displays a roadmap with actionable tasks
+
+---
+
+## 🧠 AI Approach
+
+- Uses Q-Learning
+- State: (time_slot, weakest_subject)
+- Action: next subject to study
+- Reward system encourages improvement and balance
+
+---
+
+## 🖥️ Installation & Setup
+
+### Clone the repository
+git clone https://github.com/manasvi-maheshwari/synapse-study-planner.git
+
+### Install dependencies
+pip install numpy
+
+### Run the application
+python app.py
+
+---
+
+## 🔮 Future Improvements
+
+- Data visualization
+- Web & mobile versions
+- Cloud sync
+- More customization
+
+---
+
+## 📄 License
+
+MIT License
diff --git a/510_23BAI10157_23BAI10274/app.py b/510_23BAI10157_23BAI10274/app.py
new file mode 100644
index 0000000..d3bf7ca
--- /dev/null
+++ b/510_23BAI10157_23BAI10274/app.py
@@ -0,0 +1,351 @@
+import tkinter as tk
+from tkinter import ttk
+from brain import StudyAI
+from logic import run_simulation
+
+# ── DESIGN SYSTEM ────────────────────────────────────────────────────────────
+UI = {
+    # Warm off-white / parchment palette
+    "bg":           "#F5F0E8",      # warm parchment base
+    "surface":      "#FDFAF4",      # card surface, soft cream
+    "surface2":     "#EDE8DC",      # subtle inset / sidebar bg
+    "border":       "#DDD8CC",      # gentle divider
+    "border_dark":  "#C8C0B0",      # slightly stronger border
+
+    # Terracotta + slate accent system
+    "accent":       "#C0654A",      # terracotta / rust – main CTA
+    "accent_light": "#E8927A",      # lighter hover tint
+    "accent_muted": "#F0DDD7",      # very soft tint background
+    "green":        "#4A8C6A",      # positive / growth indicator
+    "green_muted":  "#D6EAE0",      # soft green background
+
+    # Typography
+    "text_h1":      "#1E1A14",      # near-black headings
+    "text_body":    "#5A5248",      # warm mid-tone body
+    "text_muted":   "#9C9488",      # placeholder / label text
+
+    # Fonts – warm humanist choices
+    "font_logo":    ("Georgia", 17, "bold"),
+    "font_sub":     ("Georgia", 9, "italic"),
+    "font_h2":      ("Georgia", 11, "bold"),
+    "font_label":   ("Helvetica", 8),
+    "font_label_b": ("Helvetica", 8, "bold"),
+    "font_body":    ("Helvetica", 10),
+    "font_body_b":  ("Helvetica", 10, "bold"),
+    "font_number":  ("Georgia", 20, "bold"),
+    "font_pct":     ("Georgia", 13, "bold"),
+    "font_small":   ("Helvetica", 7),
+}
+
+SUBJECT_ICONS = {
+    "Python":        "⌨",
+    "Java":          "☕",
+    "DSA":           "⎇",
+    "Aptitude":      "⊞",
+    "Communication": "❋",
+}
+
+class Synapse(tk.Tk):
+    def __init__(self):
+        super().__init__()
+        self.title("Synapse — Study Planner")
+        self.geometry("1060x740")
+        self.configure(bg=UI["bg"])
+        self.resizable(True, True)
+
+        self.diffs = {
+            "Python": 0.9, "Java": 0.8, "DSA": 0.9,
+            "Aptitude": 0.5, "Communication": 0.3
+        }
+        self.subjs = list(self.diffs.keys())
+        self.ai = StudyAI(self.subjs)
+        self.ai.load()
+
+        self.vars = {s: tk.IntVar(value=40) for s in self.subjs}
+        self._setup()
+
+    def _setup(self):
+        # ── Top nav bar ──────────────────────────────────────────────────────
+        nav = tk.Frame(self, bg=UI["surface"], pady=0)
+        nav.pack(fill="x")
+
+        # Thin accent stripe at very top
+        tk.Frame(nav, bg=UI["accent"], height=3).pack(fill="x")
+
+        nav_inner = tk.Frame(nav, bg=UI["surface"], padx=48, pady=18)
+        nav_inner.pack(fill="x")
+
+        # Logo
+        logo_frame = tk.Frame(nav_inner, bg=UI["surface"])
+        logo_frame.pack(side="left")
+        tk.Label(logo_frame, text="Synapse", font=UI["font_logo"],
+                 bg=UI["surface"], fg=UI["text_h1"]).pack(side="left")
+        
+        # Nav tagline right
+        tk.Label(nav_inner, text="Personalised · AI-Powered · Actionable",
+                 font=UI["font_small"], bg=UI["surface"], fg=UI["text_muted"]).pack(side="right")
+
+        # Thin border below nav
+        tk.Frame(self, bg=UI["border"], height=1).pack(fill="x")
+
+        # ── Main layout ──────────────────────────────────────────────────────
+        main = tk.Frame(self, bg=UI["bg"])
+        main.pack(fill="both", expand=True)
+        main.columnconfigure(0, weight=0)   # sidebar fixed
+        main.columnconfigure(1, weight=1)   # content expands
+        main.rowconfigure(0, weight=1)
+
+        # ── Sidebar ──────────────────────────────────────────────────────────
+        aside = tk.Frame(main, bg=UI["surface2"])
+        aside.grid(row=0, column=0, sticky="nsew")
+        main.columnconfigure(0, minsize=300, weight=0)
+
+        aside_inner = tk.Frame(aside, bg=UI["surface2"], padx=30, pady=32)
+        aside_inner.pack(fill="both", expand=True)
+
+        tk.Label(aside_inner, text="Skill Matrix",
+                 font=UI["font_h2"], bg=UI["surface2"], fg=UI["text_h1"]).pack(anchor="w")
+        tk.Label(aside_inner, text="Rate your current confidence",
+                 font=UI["font_label"], bg=UI["surface2"], fg=UI["text_muted"]).pack(anchor="w", pady=(2, 20))
+
+        # Skill sliders
+        for s in self.subjs:
+            card = tk.Frame(aside_inner, bg=UI["surface"],
+                            highlightthickness=1, highlightbackground=UI["border"])
+            card.pack(fill="x", pady=6)
+
+            card_inner = tk.Frame(card, bg=UI["surface"], padx=14, pady=12)
+            card_inner.pack(fill="x")
+
+            # Header row
+            head = tk.Frame(card_inner, bg=UI["surface"])
+            head.pack(fill="x")
+
+            icon = SUBJECT_ICONS.get(s, "•")
+            tk.Label(head, text=f"{icon}  {s}",
+                     font=UI["font_body_b"], bg=UI["surface"], fg=UI["text_h1"]).pack(side="left")
+
+            v_lbl = tk.Label(head, text=f"{self.vars[s].get()}%",
+                             font=UI["font_pct"], bg=UI["surface"], fg=UI["accent"])
+            v_lbl.pack(side="right")
+
+            # Custom rounded-feel trough via ttk style
+            style_name = f"{s}.Horizontal.TScale"
+
+            slider = tk.Scale(
+                card_inner, from_=0, to=100, orient="horizontal",
+                variable=self.vars[s],
+                bg=UI["surface"], fg=UI["text_body"],
+                highlightthickness=0,
+                troughcolor=UI["border"],
+                activebackground=UI["accent_light"],
+                showvalue=False, width=5, sliderlength=18,
+                command=lambda v, l=v_lbl: l.config(text=f"{int(float(v))}%")
+            )
+            slider.pack(fill="x", pady=(8, 0))
+
+            # Confidence hint
+            hint_frame = tk.Frame(card_inner, bg=UI["surface"])
+            hint_frame.pack(fill="x", pady=(4, 0))
+            for label, pos in [("Beginner", "left"), ("Expert", "right")]:
+                tk.Label(hint_frame, text=label, font=UI["font_small"],
+                         bg=UI["surface"], fg=UI["text_muted"]).pack(side=pos)
+
+        # Spacer
+        tk.Frame(aside_inner, bg=UI["surface2"], height=10).pack()
+
+        # CTA button
+        btn_frame = tk.Frame(aside_inner, bg=UI["surface2"])
+        btn_frame.pack(fill="x", pady=(16, 0))
+
+        self.btn = tk.Button(
+            btn_frame, text="Generate My Roadmap →",
+            font=UI["font_body_b"],
+            bg=UI["accent"], fg="#FDFAF4",
+            relief="flat", pady=14, padx=20,
+            cursor="hand2", activebackground=UI["accent_light"],
+            activeforeground="#FDFAF4",
+            command=self.sync
+        )
+        self.btn.pack(fill="x")
+
+        tk.Label(aside_inner, text="Results update instantly each time",
+                 font=UI["font_small"], bg=UI["surface2"], fg=UI["text_muted"]).pack(pady=(8, 0))
+
+        # Vertical divider
+        tk.Frame(main, bg=UI["border"], width=1).grid(row=0, column=0, sticky="nse")
+
+        # ── Roadmap panel ────────────────────────────────────────────────────
+        right = tk.Frame(main, bg=UI["bg"])
+        right.grid(row=0, column=1, sticky="nsew")
+
+        # Panel header
+        panel_head = tk.Frame(right, bg=UI["bg"], padx=44, pady=28)
+        panel_head.pack(fill="x")
+
+        tk.Label(panel_head, text="Your Study Roadmap",
+                 font=("Georgia", 15, "bold"), bg=UI["bg"], fg=UI["text_h1"]).pack(side="left")
+
+        self.badge = tk.Label(panel_head, text="",
+                              font=UI["font_label_b"], bg=UI["green_muted"], fg=UI["green"],
+                              padx=10, pady=4)
+        self.badge.pack(side="right", pady=(4, 0))
+
+        tk.Frame(right, bg=UI["border"], height=1).pack(fill="x", padx=44)
+
+        # Scrollable content area (using canvas for scroll support)
+        self.content_frame = tk.Frame(right, bg=UI["bg"])
+        self.content_frame.pack(fill="both", expand=True, padx=44, pady=20)
+
+        # Empty state
+        self.hint_frame = tk.Frame(self.content_frame, bg=UI["bg"])
+        self.hint_frame.pack(expand=True, fill="both")
+
+        empty_box = tk.Frame(self.hint_frame, bg=UI["surface"],
+                             highlightthickness=1, highlightbackground=UI["border"],
+                             padx=40, pady=50)
+        empty_box.place(relx=0.5, rely=0.45, anchor="center")
+
+        tk.Label(empty_box, text="✦", font=("Georgia", 28),
+                 bg=UI["surface"], fg=UI["border"]).pack()
+        tk.Label(empty_box, text="No roadmap yet",
+                 font=("Georgia", 13, "bold"), bg=UI["surface"], fg=UI["text_body"]).pack(pady=(12, 4))
+        tk.Label(empty_box, text="Adjust your skill levels on the left\nand click Generate to begin.",
+                 font=UI["font_body"], bg=UI["surface"], fg=UI["text_muted"],
+                 justify="center").pack()
+
+    def sync(self):
+        # Clear content
+        for w in self.content_frame.winfo_children():
+            w.destroy()
+
+        scores = {s: self.vars[s].get() / 100.0 for s in self.subjs}
+        plan = run_simulation(self.ai, self.subjs, self.diffs, scores, explore=False)
+
+        total_gain = sum(step["gain"] for step in plan)
+        self.badge.config(text=f"  Est. total growth  +{total_gain:.0%}  ")
+
+        for i, step in enumerate(plan):
+            self._render_step(i, step, len(plan), scores)
+
+    # ── Subject-specific plan content ────────────────────────────────────────
+    PLANS = {
+        "Python": {
+            "beginner":     ["Learn variables, loops, and functions", "Complete 10 beginner exercises on HackerRank", "Build a simple calculator or to-do CLI app"],
+            "intermediate": ["Study OOP: classes, inheritance, decorators", "Practice file I/O and exception handling", "Build a REST API with Flask or FastAPI"],
+            "advanced":     ["Deep-dive into generators, asyncio, and metaclasses", "Optimise with profiling tools (cProfile, line_profiler)", "Contribute to an open-source Python project"],
+        },
+        "Java": {
+            "beginner":     ["Understand JVM basics, types, and control flow", "Practice OOP: classes, interfaces, and inheritance", "Build a simple bank account simulator"],
+            "intermediate": ["Study Collections, Generics, and Streams API", "Learn exception handling and multithreading basics", "Build a CRUD app with Spring Boot"],
+            "advanced":     ["Master concurrency: locks, executors, CompletableFuture", "Explore JVM internals and memory management", "Design a microservice with Spring Boot + Docker"],
+        },
+        "DSA": {
+            "beginner":     ["Revise arrays, strings, and basic recursion", "Solve 5 easy LeetCode problems daily", "Understand time/space complexity (Big-O)"],
+            "intermediate": ["Study linked lists, stacks, queues, and trees", "Solve medium LeetCode problems on sliding window & BFS/DFS", "Practice sorting algorithms from scratch"],
+            "advanced":     ["Master dynamic programming patterns", "Study graphs: Dijkstra, Floyd-Warshall, topological sort", "Mock interview: 2 hard problems per week timed"],
+        },
+        "Aptitude": {
+            "beginner":     ["Review percentages, ratios, and averages", "Attempt 20 questions daily from IndiaBix", "Focus on speed: use shortcuts for multiplication"],
+            "intermediate": ["Practice time & work, pipes & cisterns, profit & loss", "Attempt sectional mock tests (30 min per topic)", "Analyse mistakes and revisit weak areas weekly"],
+            "advanced":     ["Solve full-length aptitude papers (TCS, Infosys pattern)", "Work on data interpretation and logical reasoning", "Target sub-60 sec per question consistently"],
+        },
+        "Communication": {
+            "beginner":     ["Read aloud for 10 minutes daily to build fluency", "Record yourself speaking and review for filler words", "Practice introducing yourself clearly in 60 seconds"],
+            "intermediate": ["Join a group discussion or debate practice session", "Write one structured email or summary per day", "Watch TED Talks and note structure & delivery techniques"],
+            "advanced":     ["Present a 5-minute talk on a technical topic weekly", "Practise mock GDs and HR interview rounds", "Get feedback from peers and iterate on weak points"],
+        },
+    }
+
+    def _get_plan_tier(self, score):
+        if score < 0.4:   return "beginner"
+        if score < 0.7:   return "intermediate"
+        return "advanced"
+
+    def _render_step(self, i, step, total, scores):
+        subj  = step["subj"]
+        score = scores[subj]
+        tier  = self._get_plan_tier(score)
+        tasks = self.PLANS.get(subj, {}).get(tier, ["Study core concepts", "Practice problems", "Review and revise"])
+
+        # Outer card
+        card = tk.Frame(self.content_frame, bg=UI["surface"],
+                        highlightthickness=1, highlightbackground=UI["border"])
+        card.pack(fill="x", pady=6)
+
+        # ── Header row ───────────────────────────────────────────────────────
+        header = tk.Frame(card, bg=UI["surface"], padx=24, pady=16)
+        header.pack(fill="x")
+
+        # Step badge
+        num_badge = tk.Frame(header, bg=UI["accent_muted"], width=40, height=40)
+        num_badge.pack(side="left")
+        num_badge.pack_propagate(False)
+        tk.Label(num_badge, text=str(i + 1), font=("Georgia", 13, "bold"),
+                 bg=UI["accent_muted"], fg=UI["accent"]).place(relx=0.5, rely=0.5, anchor="center")
+
+        # Subject name + tier label
+        info = tk.Frame(header, bg=UI["surface"])
+        info.pack(side="left", padx=(14, 0))
+
+        icon = SUBJECT_ICONS.get(subj, "•")
+        tk.Label(info, text=f"{icon}  {subj}",
+                 font=("Georgia", 12, "bold"), bg=UI["surface"], fg=UI["text_h1"]).pack(anchor="w")
+
+        tier_colors = {
+            "beginner":     (UI["accent_muted"],  UI["accent"]),
+            "intermediate": ("#FFF3DC",            "#B07D20"),
+            "advanced":     (UI["green_muted"],    UI["green"]),
+        }
+        tc_bg, tc_fg = tier_colors[tier]
+        tk.Label(info, text=f"  {tier.capitalize()} level  ",
+                 font=UI["font_small"], bg=tc_bg, fg=tc_fg).pack(anchor="w", pady=(3, 0))
+
+        # Growth pill (right side)
+        gain_frame = tk.Frame(header, bg=UI["green_muted"],
+                              highlightthickness=1, highlightbackground="#B8DCCB")
+        gain_frame.pack(side="right")
+        gain_inner = tk.Frame(gain_frame, bg=UI["green_muted"], padx=14, pady=8)
+        gain_inner.pack()
+        tk.Label(gain_inner, text=f"+{step['gain']:.1%}",
+                 font=("Georgia", 13, "bold"), bg=UI["green_muted"], fg=UI["green"]).pack()
+        tk.Label(gain_inner, text="est. growth",
+                 font=UI["font_small"], bg=UI["green_muted"], fg=UI["green"]).pack()
+
+        # ── Divider ──────────────────────────────────────────────────────────
+        tk.Frame(card, bg=UI["border"], height=1).pack(fill="x", padx=24)
+
+        # ── Task list ────────────────────────────────────────────────────────
+        tasks_frame = tk.Frame(card, bg=UI["surface"], padx=24, pady=14)
+        tasks_frame.pack(fill="x")
+
+        tk.Label(tasks_frame, text="Recommended actions",
+                 font=UI["font_label_b"], bg=UI["surface"], fg=UI["text_muted"]).pack(anchor="w", pady=(0, 8))
+
+        for t, task in enumerate(tasks):
+            row = tk.Frame(tasks_frame, bg=UI["surface"])
+            row.pack(fill="x", pady=3)
+            tk.Label(row, text=f"{t+1}.", font=UI["font_label_b"],
+                     bg=UI["surface"], fg=UI["accent"], width=2).pack(side="left")
+            tk.Label(row, text=task, font=UI["font_body"],
+                     bg=UI["surface"], fg=UI["text_body"], anchor="w").pack(side="left", padx=(6, 0))
+
+        # ── Progress bar ─────────────────────────────────────────────────────
+        bar_frame = tk.Frame(card, bg=UI["surface"], padx=24, pady=(4, 14))
+        bar_frame.pack(fill="x")
+
+        bar_bg = tk.Frame(bar_frame, bg=UI["border"], height=4)
+        bar_bg.pack(fill="x")
+
+        fill_width = min(1.0, step["gain"] * 5)
+
+        def draw_bar(event, bg=bar_bg, fw=fill_width):
+            w = bg.winfo_width()
+            bar_fill = tk.Frame(bg, bg=UI["green"], height=4, width=int(w * fw))
+            bar_fill.place(x=0, y=0)
+
+        bar_bg.bind("<Configure>", draw_bar)
+
+
+if __name__ == "__main__":
+    Synapse().mainloop()
\ No newline at end of file
diff --git a/510_23BAI10157_23BAI10274/brain.py b/510_23BAI10157_23BAI10274/brain.py
new file mode 100644
index 0000000..3cd0cef
--- /dev/null
+++ b/510_23BAI10157_23BAI10274/brain.py
@@ -0,0 +1,28 @@
+import numpy as np
+import json
+import os
+
+class StudyAI:
+    def __init__(self, subjects, slots=6):
+        self.subjects = subjects
+        self.q = np.zeros((slots + 1, len(subjects), len(subjects)))
+
+    def choose(self, slot, weakest_idx, explore=True):
+        import random
+        if explore and random.random() < 0.2:
+            return random.randint(0, len(self.subjects) - 1)
+        return int(np.argmax(self.q[slot][weakest_idx]))
+
+    def learn(self, slot, weakest, choice, reward, next_slot, next_weakest):
+        cur = self.q[slot][weakest][choice]
+        best = np.max(self.q[next_slot][next_weakest])
+        self.q[slot][weakest][choice] += 0.1 * (reward + 0.9 * best - cur)
+
+    def save(self, file="q_table.json"):
+        with open(file, "w") as f: json.dump(self.q.tolist(), f)
+
+    def load(self, file="q_table.json"):
+        if os.path.exists(file):
+            with open(file) as f: self.q = np.array(json.load(f))
+            return True
+        return False
\ No newline at end of file
diff --git a/510_23BAI10157_23BAI10274/logic.py b/510_23BAI10157_23BAI10274/logic.py
new file mode 100644
index 0000000..4dd4b8c
--- /dev/null
+++ b/510_23BAI10157_23BAI10274/logic.py
@@ -0,0 +1,61 @@
+import random
+
+def run_simulation(ai, subjects, difficulty, scores, slots=6, explore=True):
+    current_scores = scores.copy()
+    history = []
+    last_choice = -1
+    recent_choices = []          # track last N picks for cooldown
+    subject_counts = {s: 0 for s in subjects}
+    n = len(subjects)
+
+    # Ensure slots >= number of subjects so every subject gets at least one slot
+    slots = max(slots, n)
+
+    for slot in range(slots):
+        weakest_idx = min(range(n), key=lambda i: current_scores[subjects[i]])
+
+        # --- Cooldown mask: block subjects picked in last (n-1) turns ---
+        cooldown = max(1, n - 1)
+        blocked = set(recent_choices[-cooldown:]) if len(subjects) > 1 else set()
+
+        # Force-include any subject that has had zero slots so far
+        unvisited = [i for i in range(n) if subject_counts[subjects[i]] == 0]
+        if unvisited:
+            # Pick the weakest unvisited subject
+            choice = min(unvisited, key=lambda i: current_scores[subjects[i]])
+        else:
+            # Let AI choose, but mask out recently used subjects
+            raw_choice = ai.choose(slot, weakest_idx, explore)
+            if raw_choice in blocked:
+                # Fall back to weakest subject not on cooldown
+                available = [i for i in range(n) if i not in blocked]
+                if not available:
+                    available = list(range(n))   # safety valve
+                choice = min(available, key=lambda i: current_scores[subjects[i]])
+            else:
+                choice = raw_choice
+
+        subj = subjects[choice]
+        subject_counts[subj] += 1
+
+        # --- Gain: diminishing returns as score rises ---
+        gap = 1.0 - current_scores[subj]
+        gain = gap * difficulty[subj] * random.uniform(0.12, 0.22)
+        current_scores[subj] = min(1.0, current_scores[subj] + gain)
+
+        # --- Reward shaping ---
+        reward = gain * 20
+        if choice == weakest_idx:
+            reward += 5                          # good: tackled weakest
+        if choice == last_choice:
+            reward -= 15                         # strong repetition penalty
+        if subject_counts[subj] > slots // n + 1:
+            reward -= 10                         # penalise over-concentration
+
+        ai.learn(slot, weakest_idx, choice, reward, slot + 1, weakest_idx)
+        history.append({"subj": subj, "score": current_scores[subj], "gain": gain})
+
+        recent_choices.append(choice)
+        last_choice = choice
+
+    return history
\ No newline at end of file
diff --git a/CAM DATA B21 RL.xlsx b/CAM DATA B21 RL.xlsx
deleted file mode 100644
index 4aa4fae..0000000
Binary files a/CAM DATA B21 RL.xlsx and /dev/null differ
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..ef752fc
--- /dev/null
+++ b/README.md
@@ -0,0 +1,72 @@
+# 🧠 Synapse — AI-Powered Study Planner
+
+Synapse is an intelligent, personalized study planner that uses **Reinforcement Learning (Q-Learning)** to generate optimized study roadmaps based on your strengths and weaknesses.
+
+It combines AI decision-making with a clean GUI to help you focus on what matters most.
+
+---
+
+## 🚀 Features
+
+- 🎯 Personalized Study Plan based on your current skill levels  
+- 🧠 AI-Powered Scheduling using Q-learning  
+- 📊 Adaptive Learning – improves recommendations over time  
+- 🔄 Balanced Subject Distribution (avoids repetition & burnout)  
+- 💡 Actionable Tasks for each subject (Beginner → Advanced)  
+- 🖥️ Modern GUI built with Tkinter  
+
+---
+
+## 🗂️ Project Structure
+
+.
+├── app.py
+├── brain.py
+├── logic.py
+└── q_table.json
+
+---
+
+## ⚙️ How It Works
+
+1. Input your confidence levels for each subject (0–100%)
+2. The AI identifies weak areas
+3. Generates a study sequence using reinforcement learning
+4. Displays a roadmap with actionable tasks
+
+---
+
+## 🧠 AI Approach
+
+- Uses Q-Learning
+- State: (time_slot, weakest_subject)
+- Action: next subject to study
+- Reward system encourages improvement and balance
+
+---
+
+## 🖥️ Installation & Setup
+
+### Clone the repository
+git clone https://github.com/manasvi-maheshwari/synapse-study-planner.git
+
+### Install dependencies
+pip install numpy
+
+### Run the application
+python app.py
+
+---
+
+## 🔮 Future Improvements
+
+- Data visualization
+- Web & mobile versions
+- Cloud sync
+- More customization
+
+---
+
+## 📄 License
+
+MIT License
diff --git a/app.py b/app.py
new file mode 100644
index 0000000..d3bf7ca
--- /dev/null
+++ b/app.py
@@ -0,0 +1,351 @@
+import tkinter as tk
+from tkinter import ttk
+from brain import StudyAI
+from logic import run_simulation
+
+# ── DESIGN SYSTEM ────────────────────────────────────────────────────────────
+UI = {
+    # Warm off-white / parchment palette
+    "bg":           "#F5F0E8",      # warm parchment base
+    "surface":      "#FDFAF4",      # card surface, soft cream
+    "surface2":     "#EDE8DC",      # subtle inset / sidebar bg
+    "border":       "#DDD8CC",      # gentle divider
+    "border_dark":  "#C8C0B0",      # slightly stronger border
+
+    # Terracotta + slate accent system
+    "accent":       "#C0654A",      # terracotta / rust – main CTA
+    "accent_light": "#E8927A",      # lighter hover tint
+    "accent_muted": "#F0DDD7",      # very soft tint background
+    "green":        "#4A8C6A",      # positive / growth indicator
+    "green_muted":  "#D6EAE0",      # soft green background
+
+    # Typography
+    "text_h1":      "#1E1A14",      # near-black headings
+    "text_body":    "#5A5248",      # warm mid-tone body
+    "text_muted":   "#9C9488",      # placeholder / label text
+
+    # Fonts – warm humanist choices
+    "font_logo":    ("Georgia", 17, "bold"),
+    "font_sub":     ("Georgia", 9, "italic"),
+    "font_h2":      ("Georgia", 11, "bold"),
+    "font_label":   ("Helvetica", 8),
+    "font_label_b": ("Helvetica", 8, "bold"),
+    "font_body":    ("Helvetica", 10),
+    "font_body_b":  ("Helvetica", 10, "bold"),
+    "font_number":  ("Georgia", 20, "bold"),
+    "font_pct":     ("Georgia", 13, "bold"),
+    "font_small":   ("Helvetica", 7),
+}
+
+SUBJECT_ICONS = {
+    "Python":        "⌨",
+    "Java":          "☕",
+    "DSA":           "⎇",
+    "Aptitude":      "⊞",
+    "Communication": "❋",
+}
+
+class Synapse(tk.Tk):
+    def __init__(self):
+        super().__init__()
+        self.title("Synapse — Study Planner")
+        self.geometry("1060x740")
+        self.configure(bg=UI["bg"])
+        self.resizable(True, True)
+
+        self.diffs = {
+            "Python": 0.9, "Java": 0.8, "DSA": 0.9,
+            "Aptitude": 0.5, "Communication": 0.3
+        }
+        self.subjs = list(self.diffs.keys())
+        self.ai = StudyAI(self.subjs)
+        self.ai.load()
+
+        self.vars = {s: tk.IntVar(value=40) for s in self.subjs}
+        self._setup()
+
+    def _setup(self):
+        # ── Top nav bar ──────────────────────────────────────────────────────
+        nav = tk.Frame(self, bg=UI["surface"], pady=0)
+        nav.pack(fill="x")
+
+        # Thin accent stripe at very top
+        tk.Frame(nav, bg=UI["accent"], height=3).pack(fill="x")
+
+        nav_inner = tk.Frame(nav, bg=UI["surface"], padx=48, pady=18)
+        nav_inner.pack(fill="x")
+
+        # Logo
+        logo_frame = tk.Frame(nav_inner, bg=UI["surface"])
+        logo_frame.pack(side="left")
+        tk.Label(logo_frame, text="Synapse", font=UI["font_logo"],
+                 bg=UI["surface"], fg=UI["text_h1"]).pack(side="left")
+        
+        # Nav tagline right
+        tk.Label(nav_inner, text="Personalised · AI-Powered · Actionable",
+                 font=UI["font_small"], bg=UI["surface"], fg=UI["text_muted"]).pack(side="right")
+
+        # Thin border below nav
+        tk.Frame(self, bg=UI["border"], height=1).pack(fill="x")
+
+        # ── Main layout ──────────────────────────────────────────────────────
+        main = tk.Frame(self, bg=UI["bg"])
+        main.pack(fill="both", expand=True)
+        main.columnconfigure(0, weight=0)   # sidebar fixed
+        main.columnconfigure(1, weight=1)   # content expands
+        main.rowconfigure(0, weight=1)
+
+        # ── Sidebar ──────────────────────────────────────────────────────────
+        aside = tk.Frame(main, bg=UI["surface2"])
+        aside.grid(row=0, column=0, sticky="nsew")
+        main.columnconfigure(0, minsize=300, weight=0)
+
+        aside_inner = tk.Frame(aside, bg=UI["surface2"], padx=30, pady=32)
+        aside_inner.pack(fill="both", expand=True)
+
+        tk.Label(aside_inner, text="Skill Matrix",
+                 font=UI["font_h2"], bg=UI["surface2"], fg=UI["text_h1"]).pack(anchor="w")
+        tk.Label(aside_inner, text="Rate your current confidence",
+                 font=UI["font_label"], bg=UI["surface2"], fg=UI["text_muted"]).pack(anchor="w", pady=(2, 20))
+
+        # Skill sliders
+        for s in self.subjs:
+            card = tk.Frame(aside_inner, bg=UI["surface"],
+                            highlightthickness=1, highlightbackground=UI["border"])
+            card.pack(fill="x", pady=6)
+
+            card_inner = tk.Frame(card, bg=UI["surface"], padx=14, pady=12)
+            card_inner.pack(fill="x")
+
+            # Header row
+            head = tk.Frame(card_inner, bg=UI["surface"])
+            head.pack(fill="x")
+
+            icon = SUBJECT_ICONS.get(s, "•")
+            tk.Label(head, text=f"{icon}  {s}",
+                     font=UI["font_body_b"], bg=UI["surface"], fg=UI["text_h1"]).pack(side="left")
+
+            v_lbl = tk.Label(head, text=f"{self.vars[s].get()}%",
+                             font=UI["font_pct"], bg=UI["surface"], fg=UI["accent"])
+            v_lbl.pack(side="right")
+
+            # Custom rounded-feel trough via ttk style
+            style_name = f"{s}.Horizontal.TScale"
+
+            slider = tk.Scale(
+                card_inner, from_=0, to=100, orient="horizontal",
+                variable=self.vars[s],
+                bg=UI["surface"], fg=UI["text_body"],
+                highlightthickness=0,
+                troughcolor=UI["border"],
+                activebackground=UI["accent_light"],
+                showvalue=False, width=5, sliderlength=18,
+                command=lambda v, l=v_lbl: l.config(text=f"{int(float(v))}%")
+            )
+            slider.pack(fill="x", pady=(8, 0))
+
+            # Confidence hint
+            hint_frame = tk.Frame(card_inner, bg=UI["surface"])
+            hint_frame.pack(fill="x", pady=(4, 0))
+            for label, pos in [("Beginner", "left"), ("Expert", "right")]:
+                tk.Label(hint_frame, text=label, font=UI["font_small"],
+                         bg=UI["surface"], fg=UI["text_muted"]).pack(side=pos)
+
+        # Spacer
+        tk.Frame(aside_inner, bg=UI["surface2"], height=10).pack()
+
+        # CTA button
+        btn_frame = tk.Frame(aside_inner, bg=UI["surface2"])
+        btn_frame.pack(fill="x", pady=(16, 0))
+
+        self.btn = tk.Button(
+            btn_frame, text="Generate My Roadmap →",
+            font=UI["font_body_b"],
+            bg=UI["accent"], fg="#FDFAF4",
+            relief="flat", pady=14, padx=20,
+            cursor="hand2", activebackground=UI["accent_light"],
+            activeforeground="#FDFAF4",
+            command=self.sync
+        )
+        self.btn.pack(fill="x")
+
+        tk.Label(aside_inner, text="Results update instantly each time",
+                 font=UI["font_small"], bg=UI["surface2"], fg=UI["text_muted"]).pack(pady=(8, 0))
+
+        # Vertical divider
+        tk.Frame(main, bg=UI["border"], width=1).grid(row=0, column=0, sticky="nse")
+
+        # ── Roadmap panel ────────────────────────────────────────────────────
+        right = tk.Frame(main, bg=UI["bg"])
+        right.grid(row=0, column=1, sticky="nsew")
+
+        # Panel header
+        panel_head = tk.Frame(right, bg=UI["bg"], padx=44, pady=28)
+        panel_head.pack(fill="x")
+
+        tk.Label(panel_head, text="Your Study Roadmap",
+                 font=("Georgia", 15, "bold"), bg=UI["bg"], fg=UI["text_h1"]).pack(side="left")
+
+        self.badge = tk.Label(panel_head, text="",
+                              font=UI["font_label_b"], bg=UI["green_muted"], fg=UI["green"],
+                              padx=10, pady=4)
+        self.badge.pack(side="right", pady=(4, 0))
+
+        tk.Frame(right, bg=UI["border"], height=1).pack(fill="x", padx=44)
+
+        # Scrollable content area (using canvas for scroll support)
+        self.content_frame = tk.Frame(right, bg=UI["bg"])
+        self.content_frame.pack(fill="both", expand=True, padx=44, pady=20)
+
+        # Empty state
+        self.hint_frame = tk.Frame(self.content_frame, bg=UI["bg"])
+        self.hint_frame.pack(expand=True, fill="both")
+
+        empty_box = tk.Frame(self.hint_frame, bg=UI["surface"],
+                             highlightthickness=1, highlightbackground=UI["border"],
+                             padx=40, pady=50)
+        empty_box.place(relx=0.5, rely=0.45, anchor="center")
+
+        tk.Label(empty_box, text="✦", font=("Georgia", 28),
+                 bg=UI["surface"], fg=UI["border"]).pack()
+        tk.Label(empty_box, text="No roadmap yet",
+                 font=("Georgia", 13, "bold"), bg=UI["surface"], fg=UI["text_body"]).pack(pady=(12, 4))
+        tk.Label(empty_box, text="Adjust your skill levels on the left\nand click Generate to begin.",
+                 font=UI["font_body"], bg=UI["surface"], fg=UI["text_muted"],
+                 justify="center").pack()
+
+    def sync(self):
+        # Clear content
+        for w in self.content_frame.winfo_children():
+            w.destroy()
+
+        scores = {s: self.vars[s].get() / 100.0 for s in self.subjs}
+        plan = run_simulation(self.ai, self.subjs, self.diffs, scores, explore=False)
+
+        total_gain = sum(step["gain"] for step in plan)
+        self.badge.config(text=f"  Est. total growth  +{total_gain:.0%}  ")
+
+        for i, step in enumerate(plan):
+            self._render_step(i, step, len(plan), scores)
+
+    # ── Subject-specific plan content ────────────────────────────────────────
+    PLANS = {
+        "Python": {
+            "beginner":     ["Learn variables, loops, and functions", "Complete 10 beginner exercises on HackerRank", "Build a simple calculator or to-do CLI app"],
+            "intermediate": ["Study OOP: classes, inheritance, decorators", "Practice file I/O and exception handling", "Build a REST API with Flask or FastAPI"],
+            "advanced":     ["Deep-dive into generators, asyncio, and metaclasses", "Optimise with profiling tools (cProfile, line_profiler)", "Contribute to an open-source Python project"],
+        },
+        "Java": {
+            "beginner":     ["Understand JVM basics, types, and control flow", "Practice OOP: classes, interfaces, and inheritance", "Build a simple bank account simulator"],
+            "intermediate": ["Study Collections, Generics, and Streams API", "Learn exception handling and multithreading basics", "Build a CRUD app with Spring Boot"],
+            "advanced":     ["Master concurrency: locks, executors, CompletableFuture", "Explore JVM internals and memory management", "Design a microservice with Spring Boot + Docker"],
+        },
+        "DSA": {
+            "beginner":     ["Revise arrays, strings, and basic recursion", "Solve 5 easy LeetCode problems daily", "Understand time/space complexity (Big-O)"],
+            "intermediate": ["Study linked lists, stacks, queues, and trees", "Solve medium LeetCode problems on sliding window & BFS/DFS", "Practice sorting algorithms from scratch"],
+            "advanced":     ["Master dynamic programming patterns", "Study graphs: Dijkstra, Floyd-Warshall, topological sort", "Mock interview: 2 hard problems per week timed"],
+        },
+        "Aptitude": {
+            "beginner":     ["Review percentages, ratios, and averages", "Attempt 20 questions daily from IndiaBix", "Focus on speed: use shortcuts for multiplication"],
+            "intermediate": ["Practice time & work, pipes & cisterns, profit & loss", "Attempt sectional mock tests (30 min per topic)", "Analyse mistakes and revisit weak areas weekly"],
+            "advanced":     ["Solve full-length aptitude papers (TCS, Infosys pattern)", "Work on data interpretation and logical reasoning", "Target sub-60 sec per question consistently"],
+        },
+        "Communication": {
+            "beginner":     ["Read aloud for 10 minutes daily to build fluency", "Record yourself speaking and review for filler words", "Practice introducing yourself clearly in 60 seconds"],
+            "intermediate": ["Join a group discussion or debate practice session", "Write one structured email or summary per day", "Watch TED Talks and note structure & delivery techniques"],
+            "advanced":     ["Present a 5-minute talk on a technical topic weekly", "Practise mock GDs and HR interview rounds", "Get feedback from peers and iterate on weak points"],
+        },
+    }
+
+    def _get_plan_tier(self, score):
+        if score < 0.4:   return "beginner"
+        if score < 0.7:   return "intermediate"
+        return "advanced"
+
+    def _render_step(self, i, step, total, scores):
+        subj  = step["subj"]
+        score = scores[subj]
+        tier  = self._get_plan_tier(score)
+        tasks = self.PLANS.get(subj, {}).get(tier, ["Study core concepts", "Practice problems", "Review and revise"])
+
+        # Outer card
+        card = tk.Frame(self.content_frame, bg=UI["surface"],
+                        highlightthickness=1, highlightbackground=UI["border"])
+        card.pack(fill="x", pady=6)
+
+        # ── Header row ───────────────────────────────────────────────────────
+        header = tk.Frame(card, bg=UI["surface"], padx=24, pady=16)
+        header.pack(fill="x")
+
+        # Step badge
+        num_badge = tk.Frame(header, bg=UI["accent_muted"], width=40, height=40)
+        num_badge.pack(side="left")
+        num_badge.pack_propagate(False)
+        tk.Label(num_badge, text=str(i + 1), font=("Georgia", 13, "bold"),
+                 bg=UI["accent_muted"], fg=UI["accent"]).place(relx=0.5, rely=0.5, anchor="center")
+
+        # Subject name + tier label
+        info = tk.Frame(header, bg=UI["surface"])
+        info.pack(side="left", padx=(14, 0))
+
+        icon = SUBJECT_ICONS.get(subj, "•")
+        tk.Label(info, text=f"{icon}  {subj}",
+                 font=("Georgia", 12, "bold"), bg=UI["surface"], fg=UI["text_h1"]).pack(anchor="w")
+
+        tier_colors = {
+            "beginner":     (UI["accent_muted"],  UI["accent"]),
+            "intermediate": ("#FFF3DC",            "#B07D20"),
+            "advanced":     (UI["green_muted"],    UI["green"]),
+        }
+        tc_bg, tc_fg = tier_colors[tier]
+        tk.Label(info, text=f"  {tier.capitalize()} level  ",
+                 font=UI["font_small"], bg=tc_bg, fg=tc_fg).pack(anchor="w", pady=(3, 0))
+
+        # Growth pill (right side)
+        gain_frame = tk.Frame(header, bg=UI["green_muted"],
+                              highlightthickness=1, highlightbackground="#B8DCCB")
+        gain_frame.pack(side="right")
+        gain_inner = tk.Frame(gain_frame, bg=UI["green_muted"], padx=14, pady=8)
+        gain_inner.pack()
+        tk.Label(gain_inner, text=f"+{step['gain']:.1%}",
+                 font=("Georgia", 13, "bold"), bg=UI["green_muted"], fg=UI["green"]).pack()
+        tk.Label(gain_inner, text="est. growth",
+                 font=UI["font_small"], bg=UI["green_muted"], fg=UI["green"]).pack()
+
+        # ── Divider ──────────────────────────────────────────────────────────
+        tk.Frame(card, bg=UI["border"], height=1).pack(fill="x", padx=24)
+
+        # ── Task list ────────────────────────────────────────────────────────
+        tasks_frame = tk.Frame(card, bg=UI["surface"], padx=24, pady=14)
+        tasks_frame.pack(fill="x")
+
+        tk.Label(tasks_frame, text="Recommended actions",
+                 font=UI["font_label_b"], bg=UI["surface"], fg=UI["text_muted"]).pack(anchor="w", pady=(0, 8))
+
+        for t, task in enumerate(tasks):
+            row = tk.Frame(tasks_frame, bg=UI["surface"])
+            row.pack(fill="x", pady=3)
+            tk.Label(row, text=f"{t+1}.", font=UI["font_label_b"],
+                     bg=UI["surface"], fg=UI["accent"], width=2).pack(side="left")
+            tk.Label(row, text=task, font=UI["font_body"],
+                     bg=UI["surface"], fg=UI["text_body"], anchor="w").pack(side="left", padx=(6, 0))
+
+        # ── Progress bar ─────────────────────────────────────────────────────
+        bar_frame = tk.Frame(card, bg=UI["surface"], padx=24, pady=(4, 14))
+        bar_frame.pack(fill="x")
+
+        bar_bg = tk.Frame(bar_frame, bg=UI["border"], height=4)
+        bar_bg.pack(fill="x")
+
+        fill_width = min(1.0, step["gain"] * 5)
+
+        def draw_bar(event, bg=bar_bg, fw=fill_width):
+            w = bg.winfo_width()
+            bar_fill = tk.Frame(bg, bg=UI["green"], height=4, width=int(w * fw))
+            bar_fill.place(x=0, y=0)
+
+        bar_bg.bind("<Configure>", draw_bar)
+
+
+if __name__ == "__main__":
+    Synapse().mainloop()
\ No newline at end of file
diff --git a/brain.py b/brain.py
new file mode 100644
index 0000000..6307005
--- /dev/null
+++ b/brain.py
@@ -0,0 +1,28 @@
+import numpy as np
+import json
+import os
+
+class StudyAI:
+    def __init__(self, subjects, slots=6):
+        self.subjects = subjects
+        self.q = np.zeros((slots + 1, len(subjects), len(subjects)))
+
+    def choose(self, slot, weakest_idx, explore=True):
+        import random
+        if explore and random.random() < 0.2:
+            return random.randint(0, len(self.subjects) - 1)
+        return int(np.argmax(self.q[slot][weakest_idx]))
+
+    def learn(self, slot, weakest, choice, reward, next_slot, next_weakest):
+        cur = self.q[slot][weakest][choice]
+        best = np.max(self.q[next_slot][next_weakest])
+        self.q[slot][weakest][choice] += 0.1 * (reward + 0.9 * best - cur)
+
+    def save(self, file="q_table.json"):
+        with open(file, "w") as f: json.dump(self.q.tolist(), f)
+
+    def load(self, file="q_table.json"):
+        if os.path.exists(file):
+            with open(file) as f: self.q = np.array(json.load(f))
+            return True
+        return False
\ No newline at end of file
diff --git a/logic.py b/logic.py
new file mode 100644
index 0000000..890914c
--- /dev/null
+++ b/logic.py
@@ -0,0 +1,61 @@
+import random
+
+def run_simulation(ai, subjects, difficulty, scores, slots=6, explore=True):
+    current_scores = scores.copy()
+    history = []
+    last_choice = -1
+    recent_choices = []          # track last N picks for cooldown
+    subject_counts = {s: 0 for s in subjects}
+    n = len(subjects)
+
+    # Ensure slots >= number of subjects so every subject gets at least one slot
+    slots = max(slots, n)
+
+    for slot in range(slots):
+        weakest_idx = min(range(n), key=lambda i: current_scores[subjects[i]])
+
+        # --- Cooldown mask: block subjects picked in last (n-1) turns ---
+        cooldown = max(1, n - 1)
+        blocked = set(recent_choices[-cooldown:]) if len(subjects) > 1 else set()
+
+        # Force-include any subject that has had zero slots so far
+        unvisited = [i for i in range(n) if subject_counts[subjects[i]] == 0]
+        if unvisited:
+            # Pick the weakest unvisited subject
+            choice = min(unvisited, key=lambda i: current_scores[subjects[i]])
+        else:
+            # Let AI choose, but mask out recently used subjects
+            raw_choice = ai.choose(slot, weakest_idx, explore)
+            if raw_choice in blocked:
+                # Fall back to weakest subject not on cooldown
+                available = [i for i in range(n) if i not in blocked]
+                if not available:
+                    available = list(range(n))   # safety valve
+                choice = min(available, key=lambda i: current_scores[subjects[i]])
+            else:
+                choice = raw_choice
+
+        subj = subjects[choice]
+        subject_counts[subj] += 1
+
+        # --- Gain: diminishing returns as score rises ---
+        gap = 1.0 - current_scores[subj]
+        gain = gap * difficulty[subj] * random.uniform(0.12, 0.22)
+        current_scores[subj] = min(1.0, current_scores[subj] + gain)
+
+        # --- Reward shaping ---
+        reward = gain * 20
+        if choice == weakest_idx:
+            reward += 5                          # good: tackled weakest
+        if choice == last_choice:
+            reward -= 15                         # strong repetition penalty
+        if subject_counts[subj] > slots // n + 1:
+            reward -= 10                         # penalise over-concentration
+
+        ai.learn(slot, weakest_idx, choice, reward, slot + 1, weakest_idx)
+        history.append({"subj": subj, "score": current_scores[subj], "gain": gain})
+
+        recent_choices.append(choice)
+        last_choice = choice
+
+    return history
\ No newline at end of file