Machine Learning: التصور باستخدام Matplotlib و Seaborn

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

# تصور سريع يكشف الأنماط المخفية
df = pd.read_csv("monthly_sales.csv")
fig, axes = plt.subplots(1, 2, figsize=(14, 5))
df.plot(x="month", y="revenue", ax=axes[0], title="Revenue Trend")
sns.heatmap(df.corr(numeric_only=True), annot=True, ax=axes[1])
plt.tight_layout()
plt.savefig("sales_overview.png", dpi=150)

import matplotlib.pyplot as plt

# بيانات الإيرادات الشهرية (بالألف دولار)
months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun",
          "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
revenue = [120, 135, 150, 142, 160, 175, 180, 190, 195, 200, 220, 250]

fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(months, revenue, marker="o", linewidth=2, color="#2196F3")
ax.set_title("Monthly Revenue Trend", fontsize=14)
ax.set_xlabel("Month")
ax.set_ylabel("Revenue (thousand USD)")
ax.grid(True, alpha=0.3)
ax.fill_between(months, revenue, alpha=0.1, color="#2196F3")

# تمييز الذروة
peak_idx = revenue.index(max(revenue))
ax.annotate(f"Peak: {max(revenue)}k", xy=(peak_idx, max(revenue)),
            xytext=(peak_idx-2, max(revenue)+15),
            arrowprops=dict(arrowstyle="->", color="red"))

plt.tight_layout()
plt.savefig("revenue_trend.png", dpi=150)

# Executed successfully

import matplotlib.pyplot as plt
import numpy as np

fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# Bar chart: إيرادات الفئات
categories = ["Electronics", "Clothing", "Food", "Books", "Home"]
revenue = [2500, 1450, 900, 700, 4000]
colors = ["#FF6384", "#36A2EB", "#FFCE56", "#4BC0C0", "#9966FF"]

axes[0].barh(categories, revenue, color=colors)
axes[0].set_title("Revenue by Category")
axes[0].set_xlabel("Revenue (thousand USD)")

# Scatter plot: الإنفاق الإعلاني مقابل الإيرادات
np.random.seed(42)
ad_spend = np.random.uniform(10, 100, 50)
sales = 50 + 0.8 * ad_spend + np.random.normal(0, 10, 50)

axes[1].scatter(ad_spend, sales, alpha=0.6, color="#2196F3", edgecolors="white")
axes[1].set_title("Ad Spend vs Revenue")
axes[1].set_xlabel("Ad Spend (thousand USD)")
axes[1].set_ylabel("Revenue (thousand USD)")

# إضافة خط الاتجاه
z = np.polyfit(ad_spend, sales, 1)
p = np.poly1d(z)
axes[1].plot(ad_spend, p(ad_spend), "r--", alpha=0.8, linewidth=2)

plt.tight_layout()
plt.savefig("bar_scatter.png", dpi=150)

# Executed successfully

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# توليد بيانات مبالغ الطلبات
rng = np.random.default_rng(42)
orders_normal = rng.normal(150, 50, 1000)
orders_normal = orders_normal[orders_normal > 0]  # إزالة القيم السالبة

fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# Histogram مع KDE
sns.histplot(orders_normal, bins=30, kde=True, ax=axes[0], color="#2196F3")
axes[0].set_title("Order Amount Distribution")
axes[0].set_xlabel("Amount (USD)")

# Box plot حسب الفئة
categories = rng.choice(["Electronics", "Clothing", "Food"], 500)
amounts = np.where(categories == "Electronics",
                   rng.normal(300, 80, 500),
                   np.where(categories == "Clothing",
                            rng.normal(120, 40, 500),
                            rng.normal(50, 20, 500)))
amounts = np.clip(amounts, 1, None)
data = {"category": categories, "amount": amounts}

sns.boxplot(data=data, x="category", y="amount", ax=axes[1], palette="Set2")
axes[1].set_title("Order Amount by Category")

plt.tight_layout()
plt.savefig("distribution.png", dpi=150)

# Executed successfully

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# محاكاة ارتباطات ميزات SalesPredict
np.random.seed(42)
n = 200
ad_spend = np.random.uniform(10, 100, n)
traffic = ad_spend * 1.2 + np.random.normal(0, 15, n)
conversion = traffic * 0.03 + np.random.normal(0, 0.5, n)
revenue = conversion * 150 + np.random.normal(0, 500, n)
customer_age = np.random.uniform(18, 65, n)

df = pd.DataFrame({
    "ad_spend": ad_spend,
    "traffic": traffic,
    "conversion_rate": conversion,
    "revenue": revenue,
    "customer_age": customer_age,
})

corr = df.corr()

fig, ax = plt.subplots(figsize=(8, 6))
sns.heatmap(corr, annot=True, fmt=".2f", cmap="RdBu_r",
            center=0, vmin=-1, vmax=1, ax=ax,
            square=True, linewidths=0.5)
ax.set_title("Feature Correlation Heatmap")
plt.tight_layout()
plt.savefig("correlation_heatmap.png", dpi=150)

# Executed successfully

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

rng = np.random.default_rng(42)
df = pd.DataFrame({
    "date": pd.date_range("2024-01-01", periods=90),
    "revenue": rng.normal(5000, 1000, 90).cumsum(),
    "category": rng.choice(["Elec", "Cloth", "Food"], 90),
    "orders": rng.integers(50, 200, 90),
})

fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# (1) اتجاه الإيرادات
axes[0, 0].plot(df["date"], df["revenue"], color="#2196F3")
axes[0, 0].set_title("Daily Revenue Trend")

# (2) توزيع الفئات
cat_rev = df.groupby("category")["revenue"].sum()
axes[0, 1].pie(cat_rev, labels=cat_rev.index, autopct="%1.1f%%")

# (3) توزيع الطلبات
sns.histplot(df["orders"], bins=20, kde=True, ax=axes[1, 0], color="#4CAF50")
axes[1, 0].set_title("Orders Distribution")

# (4) الإيرادات مقابل الطلبات
axes[1, 1].scatter(df["orders"], df["revenue"], alpha=0.5, c="#FF5722")
axes[1, 1].set_title("Revenue vs Orders")

plt.tight_layout()
plt.savefig("dashboard.png", dpi=150)

# Executed successfully

import seaborn as sns

# تعيين السمة العامة
sns.set_theme(style="whitegrid", palette="muted", font_scale=1.2)

# السمات المتاحة: darkgrid, whitegrid, dark, white, ticks
# لوحات الألوان المتاحة: deep, muted, pastel, bright, dark, colorblind

import matplotlib.pyplot as plt

# الطريقة 1: استخدام خط SimHei (Windows)
plt.rcParams["font.sans-serif"] = ["SimHei", "Microsoft YaHei"]
plt.rcParams["axes.unicode_minus"] = False

# الطريقة 2: استخدام تسميات إنجليزية (موصى بها للتدويل)
# احتفظ بجميع التسميات بالإنجليزية لتجنب مشكلات الخطوط عبر المنصات
fig, ax = plt.subplots()
ax.set_title("Monthly Sales Report")  # الإنجليزية للتوافق
ax.set_xlabel("Month")
ax.set_ylabel("Revenue (thousand USD)")

# Executed successfully